SVM (Support Vector Machine) & Dialog Systems 2014


Notes:

In machine learning, support vector machines (SVMs) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis.

Resources:

  • tweetyproject.org .. comprehensive collection of Java libraries for logical aspects of artificial intelligence and knowledge representation

See also:

100 Best Support Vector Machine VideosSVM (Support Vector Machine) & Dialog Systems 2011 | SVM (Support Vector Machine) & Dialog Systems 2012 | SVM (Support Vector Machine) & Dialog Systems 2013


[BOOK] Speech and Language Processing D Jurafsky, JH Martin – 2014 – cs.colorado.edu … In this text we study the vari- Dialogue system ous components that make up modern conversational agents, including language input … in the task of deciding whether a word is spelled correctly, classi- fiers such as decision trees, support vector machines, Gaussian mixture … Cited by 39 Related articles All 18 versions

Integrating sequence information in the audio-visual detection of word prominence in a human-machine interaction scenario A Schnall, M Heckmann – Proc. INTERSPEECH,( …, 2014 – mazsola.iit.uni-miskolc.hu … [6] I. Bulyko, K. Kirchhoff, M. Ostendorf, and J. Goldberg, “Error- correction detection and response generation in a spoken dialogue system,” Speech Communication, vol. 45, no. … [21] C.-C. Chang and C.-J. Lin, “LIBSVM: A library for support vector machines,” ACM Transactions … Cited by 4

Biomedical text mining: State-of-the-art, open problems and future challenges A Holzinger, J Schantl, M Schroettner, C Seifert… – … Discovery and Data …, 2014 – Springer … Journal of Biomedical Informatics 41(6), 1070–1087 (2008) CrossRef; Yeh, JF, Wu, CH, Chen, MJ: Ontology-based speech act identification in a bilingual dialog system using partial … 20(3), 273–297 (1995); Ben-Hur, A., Weston, J.: A user’s guide to support vector machines. … Cited by 4

Designing an emotion detection system for a socially intelligent human-robot interaction C Chastagnol, C Clavel, M Courgeon… – Natural Interaction with …, 2014 – Springer … J. Mariani et al. (eds.), Natural Interaction with Robots, Knowbots and Smartphones: Putting Spoken Dialog Systems into Practice, DOI 10.1007/978-1-4614-8280-2 18, © Springer Science+Business Media New York 2014 199 Page 2. 200 C. Chastagnol et al. 18.1 Introduction … Cited by 6 Related articles All 6 versions

The SJTU System for Dialog State Tracking Challenge 2 K Sun, L Chen, S Zhu, K Yu – … of the Special Interest Group on …, 2014 – anthology.aclweb.org … vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2 (3), 27. Dan Bohus and Alex Rudnicky. 2006. A K-hypotheses + Other Belief Updating Model. In Proc. of AAAI Workshop on Statistical and Empirical Approaches for Spoken Dialogue Systems. … Cited by 2

T-PICE: Twitter Personality based Influential Communities Extraction System E Kafeza, A Kanavos, C Makris… – Big Data (BigData …, 2014 – ieeexplore.ieee.org … [1], observing that MNB (Multinomial Naive Bayes) sparse model performs better than SMO (Support Vector Machines using Sequential … These studies have introduced methods of recognition of the blogger’s personality [19] or speech based dialogue system understanding a … Cited by 3

Steps towards more natural humanmachine interaction via audio-visual word prominence detection M Heckmann – submitted to 2nd Workshop on Multimodal Analyses …, 2014 – arcor.de … In: Joseph Mariani, Laurence Devillers, MGRSR (ed.) Natural Interaction with Robots, Knowbots and Smartphones – Putting Spoken Dialog Systems into Practice,, pp. 135–151. … Chang, CC, Lin, CJ: LIBSVM: A library for support vector machines. … Cited by 2

Interaction Quality Estimation in Spoken Dialogue Systems Using Hybrid-HMMs S Ultes, W Minker – 15th Annual Meeting of the Special Interest Group on …, 2014 – aclweb.org … In Pro- ceedings of the 4th International Workshop on Spo- ken Language Dialog System (IWSDS), pages 141– 150. Springer, November. … 2007. Combined support vector machines and hidden markov mod- els for modeling facial action temporal dynamics. … Cited by 1

Room localization for distant speech recognition JA Morales-Cordovilla, H Pessentheiner… – … Annual Conference of …, 2014 – 193.6.4.39 … This can confuse a lot the dialog system which can receive a correct command (such as ”open the window!”) but may associate it with the wrong room … criminant analysis (LDA and QDA) and support vector machine (SVM; which due to its binary decision there is one per room). … Cited by 2

Inferring depression and affect from application dependent meta knowledge M Kächele, M Schels, F Schwenker – Proceedings of the 4th International …, 2014 – dl.acm.org … One general approach is to in- struct a test subject to solve a specific task using a computer. An Example for this kind of data collection is the EmoRec II corpus, where a subject is playing multiple rounds of a card game using a voice controlled dialog system [59]. … Cited by 2

Hypotheses Ranking for Robust Domain Classification And Tracking in Dialogue Systems JP Robichaud, PA Crook, P Xu… – … Conference of the …, 2014 – mazsola.iit.uni-miskolc.hu … Figure 1: Schematic diagram of the experimental spoken dialog system where (a) is a domain only contextual signal, (b) is the domain … Support vector machine (SVM) mod- els [13], one per domain, make a binary classification of the utterance with respect to their respective … Cited by 1

Resolving referring expressions in conversational dialogs for natural user interfaces A Celikyilmaz, Z Feizollahi… – Proceedings of …, 2014 – anthology.aclweb.org … Abstract Unlike traditional over-the-phone spoken dialog systems (SDSs), modern dialog systems tend to have visual rendering on the device screen as an additional modal- ity to communicate the system’s response to the user. … Cited by 2

Eliciting and annotating uncertainty in spoken language H Pon-Barry, SM Shieber, NS Longenbaugh – 2014 – dash.harvard.edu … existing corpus of Wizard-of-Oz tutorial dialogues, instances of uncertainty are labeled by a sin- gle human, the dialogue system “Wizard” (Forbes … In the first step, we use an existing support vector machine classifier (Maji and Malik, 2009) to classify all the images in the MNIST … Cited by 2 Related articles All 4 versions

Dialogue Management for User-centered Adaptive Dialogue S Ultes, H Dikme, W Minker – … Workshop On Spoken Dialogue Systems ( …, 2014 – uni-ulm.de … Schmitt et al. [11] applied a Support Vector Machine [15] (SVM) for estimating the Interaction Quality achieving an un- weighted average recall of 0.59. Proceedings of 5th International Workshop on Spoken Dialog Systems Napa, January 17-20, 2014 131 Page 4. … Cited by 2 Related articles

Relevance units machine based dimensional and continuous speech emotion prediction F Wang, H Sahli, J Gao, D Jiang, W Verhelst – Multimedia Tools and …, 2014 – Springer … Naive Bayes (NB) and Support Vector Machine (SVM) have been adopted for emotion classification on acoustic features. Instead of the fixed length segment of EmoReSp, EmoVoice performs speech emotion recognition on phrase level segments. … Cited by 1

Semantic Parser Enhancement for Dialogue Domain Extension with Little Data S Zhu, L Chen, K Sun, D Zheng, K Yu – Spoken Language Technology …, 2014 – aiexp.info … Dialog state tracking is a process of estimating a distribution over all possible dialogue states in statistical spoken dialogue system [4]. Recently, in dialogue … For classifications, we use Support Vector Machines (SVMs) by the LibSVM pack- age [12] which can output probabilities … Cited by 1

Automatically identifying trouble-indicating speech behaviors in alzheimer’s disease F Rudzicz, L Chan Currie, A Danks, T Mehta… – Proceedings of the 16th …, 2014 – dl.acm.org … This will allow us to build automated dialogue systems and assessment tools that are sensitive to confusion in people with AD. … NB) to model the likelihood of an ut terance given the class with a Gaussian using maximum a posteriori training, a support vector machine (SVM) with … Cited by 1

Dialogue Act Modeling for Non-Visual Web Access V Ashok, Y Borodin, S Stoyanchev… – 15th Annual Meeting of …, 2014 – aclweb.org … WOZ is commonly used before building a dialogue system (Chotimongkol, 2008),(Ohtake et al., 2009),(Es- kenazi et al., 1999). … The classifica- tion experiments were done using Support Vector Machine (frequently used for benchmarking), J48 Decision Tree (appropriate for a … Cited by 1

Horn And Whistle Recognition Tech-niques For NAO Robots NW Backer, A Visser – Bachelor thesis, Universiteit van Amsterdam, 2014 – staff.fnwi.uva.nl … 13 3.3.4 Support Vector Machine/C-Support Vector Classifier . . . . . 14 4 Results 14 … Before classification, all analyzed data was scaled in order to produce data with zero mean and unit variance, which is needed for algorithms like support vector machines. … Cited by 1

Improving Speech Recognition through Automatic Selection of Age Group–Specific Acoustic Models A Hämäläinen, H Meinedo, M Tjalve… – … Processing of the …, 2014 – Springer … In spoken dialogue systems, an age group classifier might be useful for selecting dialogue strategies or different ways of … We implemented age group classifiers using linear kernel Support Vector Machines (SVMs) [29, 30] trained with the Sequential Minimal Optimisation (SMO … Cited by 1

A domain-independent statistical methodology for dialog management in spoken dialog systems D Griol, Z Callejas, R López-Cózar… – Computer Speech & …, 2014 – Elsevier … manager represents dialogs as a sequence of pairs (A i , U i ), where A i is the output of the dialog system (the system … definitions of such a function: a multinomial naive Bayes classifier, an n-gram based classifier, a decision tree classifier, a support vector machine classifier, a … Cited by 1 Related articles All 3 versions

Single-and multichannel whistle recognition with nao robots K Poore, S Abeyruwan, A Seekircher… – … symposium (accepted for …, 2014 – fei.edu.br Page 1. Single- and Multi-Channel Whistle Recognition with NAO Robots Kyle Poore, Saminda Abeyruwan, Andreas Seekircher, and Ubbo Visser University of Miami, Department of Computer Science, 1365 Memorial Drive … Cited by 2

User-awareness and adaptation in conversational agents V Deli?, M Gnjatovi?, N Jakovljevi?… – Facta Universitatis, …, 2014 – casopisi.junis.ni.ac.rs … G. Rigoll, M. Lang, “Speech Emotion Recognition Combining Acoustic Features and Linguistic Information in a Hybrid Support Vector Machine-Belief Network … [14] M. Gnjatovi? and V. Deli?, “A Cognitively-Inspired Method for Meaning Representation in Dialogue Systems”. … Cited by 1

Topic classification of spoken inquiries using transductive support vector machine R Torres, H Kawanami, T Matsui, H Saruwatari… – Natural Interaction with …, 2014 – Springer … In previous work, we evaluated the classification performance of three supervised methods: a support vector machine (SVM) with a radial basis … J. Mariani et al. (eds.), Natural Interaction with Robots, Knowbots and Smartphones: Putting Spoken Dialog Systems into Practice, DOI … Related articles All 5 versions

Word embeddings: A semi-supervised learning method for slot-filling in spoken dialog systems X Yang, Z Chen, J Liu – Chinese Spoken Language Processing …, 2014 – ieeexplore.ieee.org … One of the key components in spoken dialog systems is seman- tic slot-filling, a sequence tagging task. There are several state- of-the-art supervised approaches to model the slot-filling prob- lem such as conditional random fields (CRF), support vector machine (SVM) and …

Application and Evaluation of a Conditioned Hidden Markov Model for Estimating Interaction Quality of Spoken Dialogue Systems S Ultes, R ElChab, W Minker – Natural Interaction with Robots, Knowbots …, 2014 – Springer … Abstract The interaction quality (IQ) metric has recently been introduced for measuring the quality of spoken dialogue systems (SDSs) on the exchange level. While previous work relied on support vector machines (SVMs), we evaluate a conditioned hidden Markov model … Cited by 1 Related articles All 5 versions

Korean Anaphora Recognition System to Develop Healthcare Dialogue-Type Agent J Yang, Y Lee – Healthcare informatics research, 2014 – m.synapse.koreamed.org … used either for deep semantic analysis of medical free-text to extract information and knowledge in an automatic manner or to design a dialogue system which can … Predictors of medication adherence in elderly patients with chronic diseases using support vector machine models …

Analysis of an Extended Interaction Quality Corpus S Ultes, MJP Sánchez, A Schmitt, W Minker – uni-ulm.de … defined as user satisfaction annotated by expert raters—is to derive a number of interaction parameters from the dialogue system and use … The main evaluation has been conducted using a Support Vector Machine (SVM) [19] with linear Kernel in accordance to Schmitt et al. …

HALEF: an open-source standard-compliant telephony-based modular spoken dialog system–A review and an outlook D Suendermann-Oeft, V Ramanarayanan… – sail.usc.edu … J., Klein, E., Lemon, O., Oka, T.: Dipper: Description and formalisation of an information-state update dialogue system architecture. In: 4th SIGdial Workshop on Dis- course and Dialogue, pp. 115–124 (2003) 4. Chang, CC, Lin, CJ: LIBSVM: a Library for Support Vector Machines. …

Detecting Multiple Domains from User’s Utterance in Spoken Dialog System S Ryu, J Song, S Koo, S Kwon, GG Lee – uni-ulm.de … Keywords: dialog system, domain selection, domain detection, learning from positive and unlabeled examples, hierarchical clustering, Support Vector Machine 1 Introduction Spoken dialog system (SDS) provides natural language interface between human and computer. …

[BOOK] Natural Interaction with Robots, Knowbots and Smartphones: Putting Spoken Dialog Systems Into Practice J Mariani, S Rosset, M Garnier-Rizet, L Devillers – 2014 – books.google.com … Kee Tan, Dilip Kumar Limbu, Tran Anh Dung, and Haizhou Li 225 Part V Spoken Dialog Systems Components 21 22 … Bart Ons, Jort F. Gemmeke, and Hugo Van hamme 249 Topic Classification of Spoken Inquiries Using Transductive Support Vector Machine….. … All 2 versions

Re-ranking ASR Outputs for Spoken Sentence Retrieval Y Song, H Ahn, H Kim – ceur-ws.org … the 5th International Work- shop on Spoken Dialog System (2014) 6. Joachims, T.: Optimizing Search Engines Using Clickthrough Data. In: Proceed- ings of ACM SIGKDD, pp. 133-142 (2002) 7. Arens, RJ: Learning to Rank Documents with Support Vector Machines via Active …

User Input Classification for Chinese Question Answering System Y Hou, X Wang, Q Chen, M Li, C Tan – Machine Learning and Cybernetics, 2014 – Springer … Some users even use the QA system as a dialogue system. … Widely used methods for question classification include rule-based methods, Naïve Bayes [3], Support Vector Machine (SVM) [4], [5] and KNN [6]. Usually, a question classification taxonomy is designed based on the …

Evaluation of Invalid Input Discrimination Using Bag-of-Words for Speech-Oriented Guidance System H Majima, R Torres, H Kawanami, S Hara… – Natural Interaction with …, 2014 – Springer … 433–436 (2004) 2. Lee, A., Nakamura, K., Nishimura, R., Saruwatari, H., Shikano, K.: Noise robust real world spoken dialogue system using GMM based … INTERSPEECH2012, Portland, September (2012) 5. Chang, C., Lin, C.: LIBSVM: A Library for Support Vector Machines. … Related articles All 5 versions

A Study on Analysis of Bio-Signals for Basic Emotions Classification: Recognition Using Machine Learning Algorithms EH Jang, BJ Park, SH Kim, Y Eum… – … and Applications (ICISA …, 2014 – ieeexplore.ieee.org … probabilistic classifier based on applying Bayes’ theorem with strong (naive) independence assumptions, and support vector machine (SVM) which is one … Emotion recognition using bio-sensors: First step towards an automatic system,” in Affective Dialogue Systems Tutorial and …

Ordinal Regression For Interaction Quality Prediction L El Asri, H Khouzaimi, R Laroche, O Pietquin – lifl.fr … The automatic prediction of the quality of a dialogue is useful to keep track of a spoken dialogue system’s performance and, if nec- essary … We apply support vector machines for ordinal re- gression on a corpus of dialogues where each system-user exchange was given a rate on … Cited by 1 Related articles

Emotion Recognition in Real-world Conditions with Acoustic and Visual Features M Sidorov, W Minker – Proceedings of the 16th International Conference …, 2014 – dl.acm.org … Such opportunity can be useful in various applications, eg, im- provement of Spoken Dialogue Systems (SDSs) or monitor- ing agents in call-centers. … Keywords Audio-video data corpus, Facial expression, Feature-based fusion, Support vector machine. …

A Machine Learning Approach to Pronominal Anaphora Resolution in Dialogue Based Intelligent Tutoring Systems NB Niraula, V Rus – Computational Linguistics and Intelligent Text …, 2014 – Springer … mostly it, they, he and she [7]. Second, referents can be VP-antecedents or NP-antecedents in spoken dialogue systems but almost … Table 7 shows the results for the baseline, and the best results obtained for DARE++ using Naive Bayes, Support vector machine (SVM), Logistic … Related articles

A Factored Discriminative Spoken Language Understanding for Spoken Dialogue Systems F Jur?í?ek, O Dušek, O Plátek – Text, Speech and Dialogue, 2014 – Springer … and covers virtually all cities in the Czech Republic [5]. A successful SLU component in a spoken dialogue system must be … Semantic Tuple Classifiers (STC) based on support vector machines have been used to build semantic trees by recursively calling classifiers that predict …

Automatic detection of psychological distress indicators and severity assessment in crisis hotline conversations M Pacula, T Meltzer, M Crystal… – … , Speech and Signal …, 2014 – ieeexplore.ieee.org … 24, no. 4, pp. 562–588, 2010. [15] Milica Gasic, Filip Jurc?ce/, Blaise Thomson, Kai Yu and Steve Young. On-line policy optimisation of spoken dialogue systems via live interaction with human subjects. … LIBSVM: A Library for Support Vector Machines. ACM Trans. Intell. Syst. …

A Security System by using Face and Speech Detection CS Patil, GN Dhoot – 2014 – inpressco.com … Practical applications of speech recognition and dialogue systems bring sometimes a requirement to synthesize or reconstruct the speech … Cognitive Neuroscience, 1991.3(1): 71-86 KE Gates, Fast and Accurate Face Recognition Using Support Vector Machines, Proceedings of …

Computationally-efficient endpointing features for natural spoken interaction with personal-assistant systems H Arsikere, E Shriberg… – Acoustics, Speech and …, 2014 – ieeexplore.ieee.org … We collected many utterances per speaker for two reasons. First, we wanted speakers to learn that the system did not cut them off as quickly as typical speech dialog systems. … Support vector machines (SVMs), as implemented in LIBSVM [24], are used as classifiers. …

Estimating the Sentiment of Arabic Social Media F Harrag – citala.org … Rushdi-Salem et al., [17] 2011 Support Vector Machine + Naïve bayes Positive / Negative Web pages and blogs in Arabic about Review … [12] Hijjawi M. and Bander Z., “An Arabic Stemming Approach using Machine Learning with Arabic Dialogue System” in ICGST AIML-11 …

Learning Phrase Patterns for Text Classification Using a Knowledge Graph and Unlabeled Data A Marin, R Holenstein, R Sarikaya… – … Conference of the …, 2014 – mazsola.iit.uni-miskolc.hu … Using Support Vector Machine classification, we obtain improvements over lexical and fully-supervised phrase pattern features in domain and intent … tasks, such as topic clas- sification, more complex problems like intent classification or slot-filling for dialog systems often require …

VLSI Design for SVM-Based Speaker Verification System JC Wang, LX Lian, YY Lin, JH Zhao – ieeexplore.ieee.org … The support vector machine (SVM), a dis- criminative approach, has recently attracted much attention because it discriminates between the classes and … that require both the outputs of speech and speaker recognition, such as conversational spoken dialog systems and personal …

Hybrid Artificial Intelligence Systems MPAC de Carvalho, JSPM Wozniak, HQE Corchado – Springer … C. Pop, Levente Fuksz, and Andrei Horvat Marc A Framework to Develop Adaptive Multimodal Dialog Systems for Android-Based Mobile Devices….. 25 David Griol and José Manuel Molina Wind Power Ramp Event Prediction with Support Vector Machines…. …

Language Technology for eGovernment–Business Cases M Henkel, E Perjons, E Sneiders, J Karlgren… – New Perspectives in …, 2014 – Springer … al (2010) and Itakura et al (2010) use basic statistical techniques: cosine similarity, tf-idf weights, Jaccard coefficient, Support Vector Machine. … Acomb, K., Bloom, J., Dayanidhi, K., Hunter, P., Krogh, P., Levin, E., Pieraccini, R.: Technical support dialog systems: Issues, problems … Related articles All 2 versions

Using Improved BQPSO to Recognize Emotion Based on ECG* J CHENG, G LIU – Journal of Computational Information Systems, 2014 – jofcis.com … and the size of samples; 2) to extract the other effective features such as nonlinear feature to further improve the performance of system; 3) to apply and compare various classifiers, such as support vector machine (SVM), k … Affective Dialogue Systems, 2004, 3068 : 36 48. …

QAS SD Joshi – ijaiem.org … Since 1960s, a variety of natural language database frontends, dialog systems and language understanding systems were created. … The machine learning approaches to question classification [7] reported that Support Vector Machines (SVM) outperformed the rest of the …

Visual Contribution to Word Prominence Detection in a Playful Interaction Setting M Heckmann – Natural Interaction with Robots, Knowbots and …, 2014 – Springer … renders the extraction of prosodic cues more reliable and hence can be helpful to develop more natural spoken dialog systems which can … OpenCV: Computer vision with the OpenCV library O’reilly (2008) 5. Chang, CC, Lin, CJ: LIBSVM: A library for support vector machines. … Related articles All 9 versions

Automatic Recognition of Personality Traits: A Multimodal Approach M Sidorov, S Ultes, A Schmitt – Proceedings of the 2014 Workshop on …, 2014 – dl.acm.org … Adding personality-dependency may be useful to build speaker-adaptive models, eg, to improve Spoken Dialogue Systems (SDSs) or to monitor agents in call-centers. … Keywords Personality traits, audio-visual features, feature-based fu- sion, support vector machine. …

Bimodal Emotion Recognition from Speech and Text W Ye, X Fan – International Journal of Advanced Computer Science & …, 2014 – thesai.org … Two different classifiers Support Vector Machines (SVMs) and BP neural network are adopted to classify the emotional states. … This method can be applied to a telephone service center dialogue system to recognize customers’ negative emotions, such as anger, impatience etc. … Related articles All 4 versions

Emotion Recognition and Depression Diagnosis by Acoustic and Visual Features: A Multimodal Approach M Sidorov, W Minker – Proceedings of the 4th International Workshop on …, 2014 – dl.acm.org … Such opportunity can be useful in various applications, eg, improvement of Spoken Dialogue Systems (SDSs) or mon- itoring agents in call … nition using audio cues, the most common algorithms used are Multi Layer Perceptron (MLP) [25] and Support Vector Machine (SVM) [12 …

Predicting when to Laugh with Structured Classification B Piot, O Pietquin, M Geist – Fifteenth Annual Conference of the …, 2014 – 193.6.4.39 … Well known methods such as Classification Trees [9], K- Nearest Neighbors (KNN) [10] and Support Vector Machines (SVM) [11, 12] are widely used and … types of problems [20] and has already been used to imitate hu- man users in the case of spoken dialogue systems [21]. …

Joint Semantic Utterance Classification and Slot Filling with Recursive Neural Networks DZ Guo, G Tur, W Yih, G Zweig – research.microsoft.com … Spoken language understanding in human/machine spoken dialog systems aims to automatically identify the domain and intent of the user as expressed in … To this end, a number of standard classifiers can be used, such as support vector machines and boosting [8, 9]. Slot filling …

Rapidly scaling dialog systems with interactive learning JD Williams, NB Niraula, P Dasigi, A Lakshmiratan… – uni-ulm.de … Rapidly scaling dialog systems with interactive learning 3 … which match none of the intent detec- tors — can be explicitly modeled with a background model P0(y = 1|x).1 The model itself can be estimated in a variety of ways, such as boosting [9], support vector machines [5], or …

Interlocutor personality perception based on BFI profiles and coupled HMMs in a dyadic conversation MH Su, YT Zheng, CH Wu – Chinese Spoken Language …, 2014 – ieeexplore.ieee.org … to provide harmonious communication between humans and computers [3] [4] [5]. In order to endow the spoken dialogue systems with flexible and … They employed logistic recognition and support vector machine (SVM) to classify each personality trait using prosodic features. …

Emotion classification based on bio-signals emotion recognition using machine learning algorithms EH Jang, BJ Park, SH Kim, M Chung… – Information Science, …, 2014 – ieeexplore.ieee.org … probabilistic classifier based on applying Bayes’ theorem with strong (naive) independence assumptions, and support vector machine (SVM) which is one … Emotion recognition using bio-sensors: First step towards an automatic system,” in Affective Dialogue Systems Tutorial and …

[BOOK] Hybrid Artificial Intelligence Systems: 9th International Conference, HAIS 2014, Salamanca, Spain, June 11-13, 2014, Proceedings M Polycarpou, AC de Carvalho, JS Pan, M Wo?niak… – 2014 – books.google.com … C. Pop, Levente Fuksz, and Andrei Horvat Marc A Framework to Develop Adaptive Multimodal Dialog Systems for Android-Based Mobile Devices….. David Griol and José Manuel Molina Wind Power Ramp Event Prediction with Support Vector Machines…. …

The Impact of Word Alignment Accuracy on Audio-visual Word Prominence Detection PM Martin Heckmann, D Kolossa – Channels – arcor.de … References [1] D. Litman, J. Hirschberg, and M. Swerts, “Characteriz- ing and predicting corrections in spoken dialogue systems,” Computational linguistics, vol. 32, no. 3, pp. … [33] C.-C. Chang and C.-J. Lin, “LIBSVM: A li- brary for support vector machines,” ACM Trans- actions …

Predicting Personality Traits using Multimodal Information F Alam, G Riccardi – Proceedings of the 2014 ACM Multi Media on …, 2014 – sisl.disi.unitn.it … 5], speech [2] and facial expres- sions [4]. The domains include personality of the blogger [8], dialogue system, social media … Classification and Evaluation We generated our classification models using Sequential Minimal Optimization (SMO) for Support Vector Machine (SVM) [14 …

A Study On Natural Expressive Speech: Automatic Memorable Spoken Quote Detection F Koto, S Sakti, G Neubig, T Toda, M Adriani… – uni-ulm.de … Research related to spoken dialog systems has progressed from the traditional task- based frameworks to more sophisticated social agents [1] that can engage the user and expressively convey the intended message. … (NB)[11], and Support Vector Machines (SVM) [10]. …

Problematic Situation Analysis and Automatic Recognition for Chinese Online Conversational System Y Xiang, Y Zhang, X Zhou, X Wang, Y Qin – CLP 2014, 2014 – aclweb.org … 2011. LIBSVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Tech- nology, 2 (27) 1-27. … 2010. Estimation Method of User Sat- isfaction Using N-gram-based Dialogue Histo- ry Model for Spoken Dialogue System. LREC. …

Hybrid continuous speech recognition systems by HMM, MLP and SVM: a comparative study E Zarrouk, YB Ayed, F Gargouri – International Journal of Speech …, 2014 – Springer … 2004) presents a new mod- eling approach using hidden Markov models (HMM) and support vector machines (SVM) to analyse the force … Systems of au- tomatic speech recognition continuous current are based on a statistical approach which (Jelinek 1976) proposed formal … Related articles

Question Answering System S Nalawade, S Kumar, D Tiwari – ijsr.net … Since 1960s, till the field was in its infancy, a variety of natural language database front-ends, dialog systems, and language … area, and there are many papers describing systems learning approaches to question classification, such as [7] use support vector machines, a machine … Related articles

Applying a Text-Based Affective Dialogue System in Psychological Research: Case Studies on the Effects of System Behaviour, Interaction Context and Social … M Skowron, S Rank, A ?widerska, D Küster… – Cognitive …, 2014 – Springer … 9271-2. Applying a Text-Based Affective Dialogue System in Psychological Research: Case Studies on the Effects of System Behaviour, Interaction Context and Social Exclusion. Marcin … 57 ]. Affective Dialogue System. The variants … Cited by 1 Related articles All 4 versions

Hierarchical Dirichlet Process Topic Modeling for Large Number of Answer Types Classification in Open domain Question Answering S Park, D Lee, J Choi, S Ryu, Y Kim, S Kown… – Information Retrieval …, 2014 – Springer … of the rule-based approach, many researchers use supervised learning algorithms such as Support Vector Machines (SVMs) [7 … tion of Korean (NRF) [NRF-2014R1A2A1A01003041, Development of multi-party anticipatory knowledge-intensive natural language dialog system]. …

The Impact of Word Alignment Accuracy on Audio-visual Word Prominence Detection M Heckmann, P Mikias… – … Communication; 11. ITG …, 2014 – ieeexplore.ieee.org … References [1] D. Litman, J. Hirschberg, and M. Swerts, “Characteriz- ing and predicting corrections in spoken dialogue systems,” Computational linguistics, vol. 32, no. 3, pp. … [33] C.-C. Chang and C.-J. Lin, “LIBSVM: A li- brary for support vector machines,” ACM Trans- actions …

Audio-Visual Signal Processing in a Multimodal Assisted Living Environment A Karpov, L Akarun, H Yalç?n… – … Conference of the …, 2014 – mazsola.iit.uni-miskolc.hu … S., Morimoto, T., Maeda and S., Tsuruta, N., “Cough detection in spoken dialogue system for home health care”, In Proc. INTERSPEECH-2004, Jeju, Korea, 1865-1868, 2004. [18] Huynh, TH, Tran, VA and Tran, HD, “Semi-supervised tree support vector machine for online cough …

Improving domain action classification in goal-oriented dialogues using a mutual retraining method CN Seon, H Lee, H Kim, J Seo – Pattern Recognition Letters, 2014 – Elsevier … The dialogue system should analyze the context of utterance (9) in order to resolve this ambiguity. … Similarly, Surendran and Levow [19] replaced the observation probabilities of an HMM with the class probabilities of a support vector machine (SVM). Kang et al. … Related articles

Two-phase reanalysis model for understanding user intention S Kang, J Seo – Pattern Recognition Letters, 2014 – Elsevier … A conventional dialogue system consists of the following components: natural language understanding, dialogue management, and response generation … Eun [3] used a Support Vector Machine (SVM) classifier for his analysis model, which extracts features of vocabulary, Part-of … Related articles

Gaussian Processes for POMDP-Based Dialogue Manager Optimization M Gasic, S Young – IEEE/ACM Transactions on Audio, Speech and …, 2014 – dl.acm.org … Various approximations allow such a model to be used for building real-world dialog systems. … I. INTRODUCTION SPOKEN dialog systems enable human-computer interac- tion where the primary input is speech. As such they have innumerable benefits. … Related articles All 3 versions

Electromyogram as a Viable Direct Human Input for Human Machine Interfaces G Sajaysurya, JS Kumaar, M Madhu, K Prakasan – 2014 – seekdl.org … algorithm performs dimension reduction by Fisher’s Linear Discriminant Analysis (LDA) and the classification is performed by a Support Vector Machine (SVM) with … en-IN/Kinect [3] MDJ McNeill, H. Sayers, S. Wilson, and P. Mc Kevitt, “A Spoken Dialogue System for Navigation …

Question answering system: A heuristic approach V Bhoir, MA Potey – Applications of Digital Information and Web …, 2014 – ieeexplore.ieee.org … II. RELATED WORK Since 1960s, a variety of natural language database frontends, dialog systems and language understanding systems were … The machine learning approaches to question classification [7] reported that Support Vector Machines (SVM) outperformed the rest of … Related articles

Recognition and Analysis of Emotion in Indonesian Conversational Speech N Lubis, S Sakti, G Neubig, T Toda, D Lestari… – phontron.com … Emotion and its triggers in human spoken dialogue: Recognition and analysis,” in Proceedings of International Workshop on Spo- ken Dialogue Systems, 2014, pp. … [11] Chih-Chung Chang and Chih-Jen Lin, “LibSVM: a library for support vector machines,” ACM Transactions on …

Speech Emotion Recognition A Review and Related Terms E Garg, M Bahl – ijircce.com … [1] Fig.1 This can also be used in the spoken dialogue system eg at call centre applications where the support staff can handle the conversation in a more adjusting manner if the emotion of the caller is identified earlier. … 4855 A) SVM : SVM stands for support vector machine . …

Learning to recognize horn and whistle sounds for humanoid robots NW Backer, A Visser – staff.fnwi.uva.nl … Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3):27:1–27:27, 2011. … In Proceedings of the Paralinguistic Information and its Integration in Spoken Dialogue Systems Workshop, pages 125–132. …

On the use of a fuzzy classifier to speed up the Sp ToBI labeling of the Glissando Spanish corpus D Escudero-Mancebo, L Aguilar-Cuevas… – lrec-conf.org … In Dialog Systems, the identification of focalized or highlighted items can be crucial in interpreting the mes- sage from a semantic or pragmatic perspective. … of type k (in our case, k = 1 for a neural network, k = 2 for a decision tree, or k = 3 for a support vector machine) that is … Related articles All 2 versions

Semi-Supervised Learning of Statistical Models for Natural Language Understanding D Zhou, Y He – The Scientific World Journal, 2014 – hindawi.com … representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). … 2.1.2. Hidden Markov Support Vector Machines (HM-SVMs). For …

Classification of social laughter in natural conversational speech H Tanaka, N Campbell – Computer Speech & Language, 2014 – Elsevier … We report progress towards developing a sensor module that categorizes types of laughter for application in dialogue systems or social-skills training … A statistical model was then trained using a Support Vector Machine to predict the most likely category for each laugh in both … Related articles All 4 versions

A decision support system for market-driven product positioning and design N Lei, SK Moon – Decision Support Systems, 2014 – Elsevier … set. In a computer aided diagnosis setting, Acharya et al. showed that the AdaBoost outperforms: decision tree, fuzzy Sugeno classifier, k-nearest neighbor, probabilistic neural network, and support vector machine [34]. The …

Using Conditional Random Fields to Predict Focus Word Pair in Spontaneous Spoken English X Zang, Z Wu, H Meng, J Jia, L Cai – Fifteenth Annual Conference of …, 2014 – se.cuhk.edu.hk … It can also help in better textual and intention understanding for spoken dialog systems. Traditional approaches such as support vector machines (SVMs) prediction neglect the dependency between words and meet the obstacle of the imbalanced distribution of positive and …

Data-driven models for timing feedback responses in a Map Task dialogue system R Meena, G Skantze, J Gustafson – Computer Speech & Language, 2014 – Elsevier … The basic components of the Map Task dialogue system (Iteration 1) used for data collection … data-driven models: the Naïve Bayes classifier (NB) as a generative model, and three discriminative models, namely a J48 decision tree classifier, a Support Vector Machine (SVM) … Cited by 1 Related articles

Approach to Recognize Human Emotions HS Suresha, NS Chandrasekhar – 2014 – searchdl.org … [14] MF Valstar, I. Patras and M. Pantic, “Facial action unit detection using probabilistic actively learned support vector machines on tracked … Schaich and J. Williams, “Emotion recognition using bio-sensors: First steps towards an automatic system,” Affective Dialogue Systems, pp …

Intelligent Systems’ Holistic Evolving Analysis of Real-Life Universal Speaker Characteristics B Schuller, Y Zhang, F Eyben, F Weninger – mmk.e-technik.tu-muenchen.de … Most established techniques are static modelling with Support Vector Machines (SVMs) and dy- namic modelling with Hidden Markov Models (HMMs … spite them being crucial for real-life applications such as retrieval, dialogue systems and computer-mediated human- to-human …

A Mapping-Based Approach for General Formal Human Computer Interaction Using Natural Language V Letard, S Rosset, G Illouz – ACL 2014, 2014 – ling.uni-potsdam.de … LIB- SVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology Olivier Galibert. … Lightly Su- pervised Learning of Procedural Dialog System In Proceedings of the 51st Annual Meeting of the As- sociation for Computational Linguistics. …

Speech-Based Emotion Recognition: Feature Selection by Self-Adaptive Multi-Criteria Genetic Algorithm M Sidorov, C Brester, W Minker, E Semenkin – lrec-conf.org … such an opportunity might be useful in various applications, including improvement of the spoken dialogue systems (SDSs) performance or … The au- thors compared the emotion recognition performance of various classifiers: support vector machine, linear discrim- inant analysis … Related articles

User Modeling by Using Bag-of-Behaviors for Building a Dialog System Sensitive to the Interlocutor’s Internal State Y Chiba, T Nose, A Ito, M Ito – … of the Special Interest Group on …, 2014 – anthology.aclweb.org … Forbes-Riley and Litman, 2011a; Metallinou et al., 2012), preference (Pargellis et al., 2004) and familiarity with the system (Jokinen and Kanto, 2004; Rosis et al., 2006) to build natu- ral dialog system. … In this research, the support vector machine (SVM) is used as a classifier. …

Low Complexity On-Line Adaptation Techniques in Context of Assamese Spoken Query System S Shahnawazuddin, KT Deepak, BD Sarma… – Journal of Signal …, 2014 – Springer Page 1. Low Complexity On-Line Adaptation Techniques in Context of Assamese Spoken Query System S. Shahnawazuddin & KT Deepak & BD Sarma & A. Deka & SRM Prasanna & Rohit Sinha Received: 11 March 2014 /Revised … Related articles

A low complexity cluster model interpolation based on-line adaptation technique for spoken query systems S Shahnawazuddin, R Sinha – Chinese Spoken Language …, 2014 – ieeexplore.ieee.org … Consequently, such an approach is very prohibitive for on-line adaptation. In [11], the support speaker vectors are searched through a two class support vector machine -0.2 -0.1 0 0.1 0.2 0.3 0.4 -0.04 … 2687–2690. [3] JR Glass, “Challanges for spoken dialogue systems,” in Proc. …

Validating Attention Classifiers for Multi-Party Human-Robot Interaction ME Foster – workshops.acin.tuwien.ac.at … In Proceedings of ICMI-MLMI, 2009. [5] D. Bohus and E. Horvitz. Learning to predict engagement with a spoken dialog system in open-world settings. In Proceedings of SIGDial, 2009. … [7] C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Trans. … Related articles All 2 versions

Attentional regulations in a situated human-robot dialogue R Caccavale, E Leone, L Lucignano… – Robot and Human …, 2014 – ieeexplore.ieee.org … and it is composed of three layers: the lower layer contains the classifiers of the single modalities; the middle layer, the fusion engine, performs a Support Vector Machine (SVM)-based late … [18] L. Lucignano, F. Cutugno, S. Rossi, and A. Finzi, “A dialogue system for multimodal …

A Design of Memory-based Learning Classifier usign Genteic Strategy for Emotion Classification BJ Park, EH Jang, SH Kim, C Huh… – COGNITIVE 2014, The …, 2014 – thinkmind.org … learning algorithms (eg, Fisher’s Linear Discriminant, k-Nearest Neighbor algorithm, Neural Networks, and Support Vector Machine , etc.[4 … J. Williams, “Emotion Recognition Using Bio-Sensors: First Step Towards an Automatic System,” Affective Dialogue Systems, Tutorial and … Related articles

Picking the Amateur’s Mind–Predicting Chess Player Strength from Game Annotations C Scheible – anthology.aclweb.org … We pursue two different machine learning approaches based on support vector machines (SVMs) to predicting chess strength: classification and ranking. The simplest way to approach the problem is classification. … (2008)) or dialog systems (eg, Komatani et al. (2003)). …

Class-specific multiple classifiers scheme to recognize emotions from speech signals A Milton, S Tamil Selvi – Computer Speech & Language, 2014 – Elsevier … The classification performance of the features of AR parameters is studied using discriminant, k-nearest neighbor (KNN), Gaussian mixture model (GMM), back propagation artificial neural network (ANN) and support vector machine (SVM) classifiers and we find that the features … Cited by 3 Related articles All 3 versions

Attentional Top-Down Regulations in a Situated Human-Robot Dialogue R Caccavale, A Finzi, L Lucignano, S Rossi, M Staffa – 2014 – workshops.acin.tuwien.ac.at … modalities; the middle layer, the fusion engine, performs a Support Vector Machine (SVM)-based late fusion and pro- vides a context-free … 4. CONCLUSIONS In this paper we presented a novel human-robot interac- tion system that combines a dialogue system with top-down … Related articles All 2 versions

Mobile Student Modeling E Alepis, M Virvou – Object-Oriented User Interfaces for Personalized …, 2014 – Springer … To this end, artificial intelligence algorithmic approaches include Neural Networks (Stathopoulou and Tsihrintzis 2007 ), Support Vector Machines (Lampropoulos et al. … In: Kobsa A, Wahlster W (eds) User models in dialog systems. … Related articles

Computer Vision based Attentiveness Detection Methods in E-Learning SA Narayanan, MR Kaimal, K Bijlani… – Proceedings of the …, 2014 – dl.acm.org … (2011) Skin conductance, blood volume pressure, EEG Engagement, confusion, boredom, hopefulness Support Vector Machine, K-Nearest … markers Negative, Neutral & Positive emotions Comparison of multiple classifiers Physics Intelligent Tutoring Dialogue System ITSPOKE …

Computational Discourse Analysis M Dascalu – Analyzing Discourse and Text Complexity for Learning …, 2014 – Springer … 2009), Support Vector Machines and Collin’s perceptron (Joshi and Rosé 2007), the TagHelper environment (Rosé et al. 2008) and the semantic distances from the lexicalized ontology WordNet (Adams and Martell 2008; Dong 2006). … Related articles

An Impact Analysis of Features in a Classification Approach to Irony Detection in Product Reviews K Buschmeier, P Cimiano, R Klinger – 2014 – roman-klinger.de … However, this feature may be a strong indicator for irony. Page 4. We use a support vector machine (SVM, Cortes and Vapnik (1995)) with a linear kernel in the im- plementation provided by libSVM (Fan et al., 2005; Chang and Lin, 2011). … Cited by 1 Related articles All 2 versions

Application of Deep Belief Networks for Natural Language Understanding. R Sarikaya, GE Hinton… – IEEE/ACM Transactions …, 2014 – learning.cs.toronto.edu … C. Support Vector Machines Support vector machines (SVMs) are supervised learning methods used for classification. … Ruhi Sarikaya Dr. Ruhi Sarikaya is a principal scientist and the manager of language understanding and dialog systems group at Microsoft. … Cited by 3 Related articles All 4 versions

Task Estimation Using Latent Semantic Analysis of Visual Scenes and Spoken Words M Kimura, S Sawada, Y Iribe… – Electronics and …, 2014 – Wiley Online Library … For example, it has been pointed out in the field of text classification that an advanced classification capability can be achieved by using support vector machines and other algorithms for word categorization in a vector space [18]. REFERENCES. … Related articles All 2 versions

Robo fashion world: a multimodal corpus of multi-child human-computer interaction JF Lehman – Proceedings of the 2014 workshop on Understanding …, 2014 – dl.acm.org Page 1. Robo Fashion World: A Multimodal Corpus of Multi-child Human-Computer Interaction Jill Fain Lehman Disney Research, Pittsburgh 4720 Forbes Avenue, Suite 110 Pittsburgh, PA 15217, USA 1-412-688-3405 jill.lehman@disneyresearch.com …

POSTECH Immersive English Study (POMY): Dialog-Based Language Learning Game LEE Kyusong, SO Kweon, LEE Sungjin… – … on Information and …, 2014 – search.ieice.org … because the perplexity of the dialog is higher in chat-oriented dia- log systems, because the domain is already decided in the information-seeking dialog systems so that … The LIBSVM [45] Support Vector Machine classifier is used to produce a model that predicts grammaticality. …

Context Awareness and Personalization in Dialogue Planning RWH Fisher – 2014 – cs.cmu.edu … to discourse pars- ing, with much of the recent work utilizing comparatively simple models such as linear support vector machines and maximum … it may be preferable to use inverse reinforcement learning (also called imitation learning) to train a dialogue system using human … Related articles All 2 versions

Text Mining of Business-Oriented Conversations at a Call Center H Takeuchi, T Yamaguchi – Data Mining for Service, 2014 – Springer … (2002). 9. Walker, MA, Langkilde-Geary, I., Hastie, HW, Wright, J., Gorin, A.: Automatically training a problematic dialogue predictor for a spoken dialogue system. J. Artif. Intell. … Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. … Related articles All 2 versions

Automatic music “listening” for automatic music performance: a grandpiano dynamics classifier D Di Carlo, A Roda – samp.dei.unipd.it … on Speech and Audio Processing, 2002, 10(5), 293-302 5. Danisman, T., Alpkocak, A.: “Emotion Classification of Audio Signals Using En- semble of Support Vector Machines”, Perception in Multimodal Dialogue Systems, Springer Berlin Heidelberg, Berlin, 2008. …

Investigation of Speaker Group-Dependent Modelling for Recognition of Affective States from Speech I Siegert, D Philippou-Hübner, K Hartmann, R Böck… – Cognitive …, 2014 – Springer … T, Maier A, Bauer J, Burkhardt F, Noth E. Age and gender recognition for telephone applications based on GMM supervectors and support vector machines. … Gnjatovi? M, Rösner D. On the role of the NIMITEK corpus in developing an emotion adaptive spoken dialogue system. …

[BOOK] PRICAI 2014: Trends in Artificial Intelligence: 13th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2014, Gold Coast, QLD, Australia, … DN Pham, SB Park – 2014 – books.google.com … Nina Narodytska, and Toby Walsh A Multi-objective Genetic Algorithm for Model Selection for Support Vector Machines….. … 849 Noriko Otani, Shoko Shirakawa, and Masayuki Numao Dialogue Management in Spoken Dialogue System with Visual …

Emotion recognition from facial EMG signals using higher order statistics and principal component analysis S Jerritta, M Murugappan, K Wan… – Journal of the Chinese …, 2014 – Taylor & Francis … Wageningen: Noldus Information Technology. View all references). Researchers have also developed methodologies based on Tabu Search, Sequential Backward Selection, Neural Network, Support Vector Machine, etc. to classify the various emotional states. … Related articles All 6 versions

Natural Language Understanding and Prediction: from Formal Grammars to Large Scale Machine Learning N Duta – Fundamenta Informaticae, 2014 – IOS Press … The directed dialog systems added a dialog manager and a small number of fixed intents often specified as a single piece of … Examples include Neural Networks, Support Vector Machines, AdaBoost (AT&T SLU system [23]), Condi- tional Random Fields (Microsoft NLU [10]). … Cited by 1 Related articles All 2 versions

Statistical post-editing and quality estimation for machine translation systems H Bechara – 2014 – doras.dcu.ie … 57 5.1 Introduction . . . . . 57 5.2 Data Set . . . . . 58 5.3 Support Vector Machines . . . . . 59 5.3.1 Classification . . . . . 59 5.3.2 Regression . . . . . … Related articles

A comparative study of evolving fuzzy grammar and machine learning techniques for text categorization NM Sharef, T Martin, KA Kasmiran, A Mustapha… – Soft Computing, 2014 – Springer … Grammar (EFG) and evalu- ates its performance against the conventional ML methods of Naïve Bayes, support vector machine, k-nearest … It has been utilized in many applications, such as dialogue systems, named entity recognition, information retrieval, and text categorization …

Semantic Similarity Calculation of Short Texts Based on Language Network and Word Semantic Information Z Zhan, F Lin, X Yang – Advanced Computer Architecture, 2014 – Springer … results [2]. In mail message processing, it can implement mail classification faster [3]. In the interface development of nature language database, it can extend the inquiry interface [4]. Moreover, it also has important applications in health advisory dialogue system [5], property …

Understanding questions and finding answers: semantic relation annotation to compute the Expected Answer Type V Petukhova – Proceedings 10th Joint ISO-ACL SIGSEM Workshop on …, 2014 – sigsem.uvt.nl … The system has all components that any traditional dialogue system has: Automatic Speech Recog- nition (ASR) and Speech Generation … used were statistical ones, namely, Conditional Random Fields (CRF)(Lafferty et al., 2001) and Support Vector Machines (SVM)(Joachims … Related articles

Towards Argument Mining from Dialogue K Budzynska, M Janier, J Kang, C Reed, P Saint-Dizier… – comma2014.arg.dundee.ac.uk … 1 has for a long time been studied in the spoken dialogue systems community, where the dialog management component decided on the system’s next move on the basis of the dialog act it assigned to the … Hilda: A discourse parser using support vector machine classification. …

A Novel Feature Selection Strategy for Enhanced Biomedical Event Extraction Using the Turku System J Xia, AC Fang, X Zhang – BioMed research international, 2014 – hindawi.com … Mainly, there are two parameters in grid searching at the training stage. The first parameter is in polynomial kernel function of support vector machine. Second, in order to set a proper precision-recall trade-off, a parameter ( ) is introduced in trigger detection. … Related articles All 6 versions

Evaluating Coherence in Open Domain Conversational Systems R Higashinaka, T Meguro… – … Conference of the …, 2014 – mazsola.iit.uni-miskolc.hu … sys- tems allow users to talk freely on a wide range of topics, they can be used to entertain users [22], build long-term relationships with users [3], or to enhance task-oriented dialogue systems by making … The tag is estimated using our support vector machine based classifier. …

Framework for Data Processing A Osherenko – Social Interaction, Globalization and Computer-Aided …, 2014 – Springer … Most prominent examples of classifiers are Support Vector Machines (SVM) that provide an analytical algorithm to classify information (Joachims 1999) or the prob- abilistic NaïveBayes algorithm that relies … 6.3.2), the button Dialog system starts a sequential dialog system (Sect. … Related articles

Machine Learning for Social Multiparty Human–Robot Interaction S Keizer, M Ellen Foster, Z Wang… – ACM Transactions on …, 2014 – dl.acm.org … JRip. Implements the RIPPER propositional rule learner [Cohen 1995]. LibSVM. Generates a Support Vector Machine using LIBSVM [Chang and Lin 2011]. Logistic. Multinomial logistic regression with a ridge estimator [le Cessie and van Houwelingen 1992]. NaiveBayes. … Cited by 1

Bilingual and Cross Domain Politics Analysis RA Jean-Valere Cossu, AM Mena… – Avances en la Ingeniería … – rcs.cic.ipn.mx … 5 Multi-Class Support Vector Machine, see: http://www.cs.cornell.edu/people/tj/ svm_light/ svm_multiclass.html 6 Stop-list from Oracle (http://docs … Fabbrizio GD, Tur G and Hakkani-Tür D 2004 Bootstrapping spoken dialogue systems with data reuse Proceedings of the 5th SIGdial …

Domain and Subtask-Adaptive Conversational Agents to Provide an Enhanced Human-Agent Interaction D Griol, JM Molina, AS de Miguel – Advances in Practical Applications of …, 2014 – Springer … of obtaining sen- tences in natural language from the non-linguistic, internal representation of information handled by the dialog system. … a function: a multinomial naive Bayes classifier, an n-gram based classifier, a decision tree classifier, a support vector machine classifier, a … Cited by 1 Related articles All 2 versions

A Graph-Based Spoken Dialog Strategy Utilizing Multiple Understanding Hypotheses N Kitaoka, Y Kinoshita, S Hara, C Miyajima… – Information and Media …, 2014 – jlc.jst.go.jp … Gruenstein [Gruenstein 08] proposed a response selection method using support vector machine (SVM) classifier trained on acoustic and lexical features … MDPs (POMDPs) have also been used for modeling the uncertainty inher- ent in spoken dialog systems [Williams 07]. … Related articles All 3 versions

Analysis of speech under stress and cognitive load in USAR operations M Charfuelan, GJ Kruijff – Natural Interaction with Robots, Knowbots and …, 2014 – Springer … (eds.), Natural Interaction with Robots, Knowbots and Smartphones: Putting Spoken Dialog Systems into Practice … Since the data is very unbalanced, a weighted support vector machine (SVM) classifier is used; weight values are determined by the proportion of data in each class … Cited by 3 Related articles All 6 versions

Exploiting Psychological Factors for Interaction Style Recognition in Spoken Conversation WL Wei, CH Wu, JC Lin, H Li – IEEE/ACM Transactions on Audio, Speech …, 2014 – dl.acm.org … by using the linear combination through the likelihoods, which result from the prosodic and linguistic Support Vector Machines (SVMs), respectively. … WORK Automatically extracting social meaning and intention from spoken dialogue is a crucial task for dialogue systems and so … Cited by 2 Related articles All 2 versions

Fuzzy logic and neural network based gender classification using three features K Meena, KR Subramaniam, M Gomathy – International Journal of …, 2014 – Inderscience … Using Sequential Floating Forward Selection (SFFS) for feature selection, Probabilistic Neural Networks (PNNs), Support Vector Machines (SVMs), the K nearest neighbour (K-NN) and Gaussian Mixture Model (GMM), as different … For achieving high-quality dialogue systems. … Related articles

The dynamics of emotional chats with bots: experiment and agent-based simulations B Tadic, V Gligorijevic, M Skowron… – arXiv preprint arXiv: …, 2014 – arxiv.org … The Affective Dialog System (DS) communicates with users in a textual modality, in a natural language form, and uses integrated … conducted with a set of natural language processing and affective processing tools and resources, including: Support Vector Machine Based Dialog … Cited by 2 Related articles All 3 versions

The dynamics of emotional chats with Bots: Experiment and agent-based simulations B Tadi?a, V Gligorijevi?a, M Skowronc, M Šuvakova – researchgate.net … Experiment with Affective Dialog System The Affective Dialog System (DS) communicates with users in a textual modality, in a … conducted with a set of natural language processing and affective processing tools and resources, including: Support Vector Machine Based Dialog …

Efficient people re-identification based on models of human clothes S Hommel, D Malysiak, U Handmann – 15th IEEE International …, 2014 – handmann.net … This vector is used as the input for a support vector machine (SVM) which is trained for people detection (we will refer to this as a HOG iteration). … A. General Feature The used general features are basically described in [2] for a human robot dialog system. … Cited by 1

Comparing multi-label classification with reinforcement learning for summarisation of timeseries data D Gkatzia, H Hastie, O Lemon – 52nd Annual Meeting of the …, 2014 – ling.uni-potsdam.de … We compared the RAkEL algorithm with single- label (SL) classification. Different SL classifiers were trained using WEKA: JRip, Decision Trees, Naive Bayes, k-nearest neighbour, logistic regres- sion, multi-layer perceptron and support vector machines. … Cited by 1

Effects of a New Voicing Parameter on Pathological Voice Discrimination by SVM A Belhaj, A Bouzid, N Ellouze – ijcit.com … The most important classifiers used in the speech recognition are considered in the classification of pathological voices as: neural networks, the Gaussian mixture model (GMM), the hidden Markov model (HMM), and the support vector machines (SVM). …

An Event Driven Fusion Approach for Enjoyment Recognition in Real-time F Lingenfelser, J Wagner, E André… – Proceedings of the …, 2014 – dl.acm.org … KinectTM. Both feature extraction steps are calculated within the SSI framework (section 4). As compu- tational model for classification, we use LibSVM’s support vector machines [1] with a linear kernel. Segmentation Based …

A system for feature classification of emotions based on Speech Analysis; Applications to Human-Robot Interaction M Rabiei, A Gasparetto – Robotics and Mechatronics (ICRoM), …, 2014 – ieeexplore.ieee.org … future, the emotional speech research will use Hidden Markov Models (HMMs), Neural Networks (NNs), Support Vector Machines (SVMs) or … Anger Classification in German and English IVR Portals,” in First International Workshop on Spoken Dialogue Systems (IWSDS), 2009. …

Objective model assessment for short-term anxiety recognition from blood volume pulse signal W Handouzi, C Maaoui, A Pruski… – … Signal Processing and …, 2014 – Elsevier … recognition system rate. Keywords. Anxiety recognition; Blood volume pulse signal; Exposure to virtual reality (EVR); Features selection; Support vector machines (SVM); Objective assessment. 1. Introduction. The handicap caused …

Effective Hand Gesture Classification Approaches P Premaratne – Human Computer Interaction Using Hand Gestures, 2014 – Springer … This can be achieved by decorrelating data using Eigen vectors and Karhunen Loeve Trans- form which are used in Support Vector Machines (SVM) or Principal Component Analysis (PCA). Page 2. … 5.2.1.3 Linear Support Vector Machine (SVM) … Cited by 1 Related articles

Tweety: A Comprehensive Collection of Java Libraries for Logical Aspects of Artificial Intelligence and Knowledge Representation M Thimm – 2014 – colonyofmalice.de … It contains also an implementa- tion of support vector machines utilizing LIBSVM15 … On top of this implementation of the actual dialogue system a sim- ulation framework was implemented that allows the (ran- dom) generation of the above multi-agent systems and mea- sures the … Cited by 3 Related articles All 3 versions

Learning Individual Behavior in an Educational Game: A Data-Driven Approach SJ Lee, YE Liu, Z Popovic – homes.cs.washington.edu … If any features are selected, we learn a supervised learning classifier using the LEARnClAssiFiER. We used a multi-class logistic regressor as a classifier, because it gives a natural proba- bilistic interpretation unlike decision trees or support vector machines. … Cited by 1 Related articles All 2 versions

An attribute detection based approach to automatic speech processing SM Siniscalchi, CH Lee – Loquens, 2014 – loquens.revistas.csic.es … This number, often referred to as a CM, serves as a reference guide for the dialogue system to provide an appropriate … detectors can be realized in several ways, eg, with artificial neural networks (ANNs), Gaussian mixture models (GMMs), support vector machines (SVM), etc. …

No Bad Feelings: Distant Supervision helps Subjectivity but not Sentiment Analysis of Arabic Twitter Feeds E Refaee, V Rieser – macs.hw.ac.uk … best performing scheme, namely Support Vector Machines (SVMs). … Her research is at the intersection of Machine Learning and Natural Language Processing, with applications ranging from multi-modal interaction, Spoken Dialogue Systems, Natural Language Generation, multi …

Cultural Differences in Playing Repeated Ultimatum Game Online with Virtual Humans E Nouri, D Traaum – System Sciences (HICSS), 2014 47th …, 2014 – ieeexplore.ieee.org … We performed a support vector machine (SVM) classification with parameters C and ? optimized through grid search. … [27] Morbini, F., Forbell, E., DeVault, D., Sagae, K., Traum, D., and A. Rizzo (2012), A Mixed-Initiative Conversational Dialogue System for Healthcare. … Related articles All 5 versions

Speech emotion recognition using amplitude modulation parameters and a combined feature selection procedure A Mencattini, E Martinelli, G Costantini… – Knowledge-Based …, 2014 – Elsevier Speech emotion recognition (SER) is a challenging framework in demanding human machine interaction systems. Standard approaches based on the categorical model o. Cited by 1 Related articles

Social signal classification using deep BLSTM recurrent neural networks R Brueckner, B Schulter – Acoustics, Speech and Signal …, 2014 – ieeexplore.ieee.org … between four types of non-verbal vocalisations – laughter, breathing, hesitation, and consent – using HMMs, Support Vector Machines (SVMs), and … the Recognition of Non-Verbal Vocalisations in Conversational Speech,” in Perception in Multimodal Dialogue Systems: 4th IEEE …

Social Power in Interactions V Prabhakaran – 2014 – blog.narotama.ac.id … For example, if a dialog system is engineered to behave appropriately given the user’s expectation of relative power, then the user may experience the interaction with the system to be more natural. … 2009) presents a multi-class classifier based on support vector machines (SVMs …

Structural information aware deep semi-supervised recurrent neural network for sentiment analysis W Rong, B Peng, Y Ouyang, C Li, Z Xiong – Frontiers of Computer Science – Springer … Another popular method is TSVM [52], which is a support vector machine (SVM) based model benefiting from both la- belled and unlabelled data and minimizes the following ob- jective function: min w,?l,?? u { 1 2 wTw + C n ? l=1 ?l + C? d ? u=1 ?? u} …

Verification Based ECG Biometrics With Cardiac Irregular Conditions Using Heartbeat Level And Segment Level Information … M Li, X Li – jie.sysu.edu.cn … ECG biometrics modeling meth- ods for the aforementioned features rely on supervised classifiers (K nearest neighbor, support vector machine, neural networks … Emotion recogni- tion using bio-sensors: First steps towards an automatic system,” in Affective Dialogue Systems, pp. … Related articles

Investigating Automatic & Human Filled Pause Insertion for Speech Synthesis R Dall, M Tomalin, M Wester… – … Annual Conference of …, 2014 – homepages.inf.ed.ac.uk … results for initial at- tempts at FP insertion prediction using ngram LMs, a recurrent neural network (RRN) LM, support vector machines (SVMs) and … of sentences with and without FPs and were asked whether the FP increased the naturalness of a voice for a dialogue system. …

Acoustic and lexical representations for affect prediction in spontaneous conversations H Cao, A Savran, R Verma, A Nenkova – Computer Speech & Language, 2014 – Elsevier In this article we investigate what representations of acoustics and word usage are most suitable for predicting dimensions of affect—arousal, valance, power. Cited by 1 Related articles

Towards the Automatic Recommendation of Musical Parameters based on Algorithm for Extraction of Linguistic Rules F Castro Espinoza, O López-Ortega… – Computación y …, 2014 – cys.cic.ipn.mx … of 1,000 songs was employed. Also, tests of dif- ferent classification methods, configurations and optimizations have been conducted, showing that Support Vector Machines perform best. However, the researchers restrict the …

State of Research of Speech Recognition M Sarma, KK Sarma – … -Based Speech Segmentation using Hybrid Soft …, 2014 – Springer … 17. In 2013, a work has been reported by Mohan et al. [ 69 ] where a spoken dialog system is designed to … Support Vector Machine (SVM) classifier is used as classifier, and broadcast news corpus of three Indian languages Tamil, Telugu, and Marathi is used [ 81 ]. 5. Paul et al. … Related articles

Emotion classification in Parkinson’s disease by higher-order spectra and power spectrum features using EEG signals: A comparative study R Yuvaraj, M Murugappan, NM Ibrahim… – Journal of integrative …, 2014 – World Scientific … In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classi?ers using the features derived from HOS and from the power spectrum. … 3.3.2. Support vector machine … Cited by 4 Related articles All 5 versions

Introduction To Emotion Recognition A Konar, A Halder… – Emotion Recognition: A …, 2014 – media.johnwiley.com.au … respectively, using Support Vector Machine (SVM) classifier first and then improv- ing the performance (Fusion of Scores) by linear logistical regression (LLR). … They employed Emotion-Profile Support Vector Machines (EP-SVM) to obtain classification accuracy of 68.2%. …

Emotion Prediction from Physiological Signals: A Comparison Study Between Visual and Auditory Elicitors F Zhou, X Qu, JR Jiao… – Interacting with …, 2014 – iwc.oxfordjournals.org We use cookies to enhance your experience on our website. By continuing to use our website, you are agreeing to our use of cookies. You can change your cookie settings at any time. Find out more. Skip Navigation. … Cited by 1 Related articles All 3 versions

Unsegmented Dialogue Act Annotation and Decoding with N-Gram Transducers C Martinez-Hinarejos, J Benedi, V Tamarit – ieeexplore.ieee.org … Abstract—Most studies on dialogue corpora, as well as most dialogue systems, employ dialogue acts as the basic units for interpreting discourse structure, user input and system actions. … Index Terms—Spoken dialogue systems, dialogue annotation, n-gram transducer …

Associating targets with SentiUnits: a step forward in sentiment analysis of Urdu text AZ Syed, M Aslam, AM Martinez-Enriquez – Artificial Intelligence Review, 2014 – Springer Page 1. Artif Intell Rev DOI 10.1007/s10462-012-9322-6 Associating targets with SentiUnits: a step forward in sentiment analysis of Urdu text Afraz Z. Syed · Muhammad Aslam · Ana Maria Martinez-Enriquez © Springer Science+Business Media BV 2012 … Cited by 2 Related articles All 4 versions

Knowledge Discovery Through Spoken Dialog A Pappu – 2014 – speech.cs.cmu.edu … All processes in a dialog system pipeline, such as recognition, parsing and dialog management, depend on the words in an input. … 1.1 Handling New Information Spoken Dialog Systems are challenged by misunderstandings and non- understanding errors. …

Structured Adaptive Regularization of Weight Vectors for a Robust Grapheme-to-Phoneme Conversion Model S SAKTI, G NEUBIG, T Tomoki… – … on Information and …, 2014 – search.ieice.org … lan- guage processing (NLP), it has been shown that because rare features are often very informative in NLP, online learning algorithms that have that property have been shown to out- perform batch learning of maximum entropy classifiers and support vector machines [22]. … Related articles All 3 versions

Pattern Recognition for Biometrics and Bioinformatics KL Du, MNS Swamy – Neural Networks and Statistical Learning, 2014 – Springer … One of the key tasks of spoken-dialog systems is classification. Gait is an efficient biometric feature for human identification at a distance. … Towards improving fuzzy clustering using support vector machine: Application to gene expression data. … Related articles

Semantic mapping for mobile robotics tasks: A survey I Kostavelis, A Gasteratos – Robotics and Autonomous Systems, 2014 – Elsevier … ICP alignment of the dominant planes. Then, a bag of features technique along with a support vector machine (SVM) is applied to accurately recognize multiple dissimilar places, as shown in Fig. 6. The second RGB-D based …

Formal Description of Arabic Syntactic Structure in the Framework of the Government and Binding Theory H Bassam, M Asma, O Nadim… – Computación y …, 2014 – polibits.cidetec.ipn.mx Page 1. Formal Description of Arabic Syntactic Structure in the Framework of the Government and Binding Theory Hammo Bassam1, Moubaiddin Asma2, Obeid Nadim1, and Tuffaha Abeer1 1 KASIT, CIS Department, 2 Department …

SemEval-2014 Task 6: Supervised Semantic Parsing of Robotic Spatial Commands K Dukes – SemEval 2014, 2014 – aclweb.org … of applica- tions, including question answering (Kwiat- kowski et al., 2013; Krishnamurthy and Mitchell, 2012), dialog systems (Artzi and … Interpretation for Semantic Parsing) is a trainable semantic parser (Kate and Mooney, 2006) that uses Support Vector Machines (SVMs) as …

Analysis and predictive modeling of body language behavior in dyadic interactions from multimodal interlocutor cues Z Yang, A Metallinou, S Narayanan – 2014 – ieeexplore.ieee.org Page 1. 1766 IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 16, NO. 6, OCTOBER 2014 Analysis and Predictive Modeling of Body Language Behavior in Dyadic Interactions From Multimodal Interlocutor Cues Zhaojun Yang … Cited by 1

A Survey on Mobile Affective Computing S Zhang, P Hui – arXiv preprint arXiv:1410.1648, 2014 – arxiv.org … To investigate if person-independent emotion recognition models could reach similar results, three modelling algorithms were selected: Discriminant Analysis (DA), Artificial Neural Network (ANN) with Back Propagation and Support Vector Machine (SVM) classifiers. …

Interpreting Natural Language Instructions Using Language, Vision, and Behavior L Benotti, T Lau, M Villalba – ACM Transactions on Interactive Intelligent …, 2014 – dl.acm.org Page 1. 13 Interpreting Natural Language Instructions Using Language, Vision, and Behavior LUCIANA BENOTTI, Universidad Nacional de Córdoba, Argentina/CONICET, Argentina TESSA LAU, Savioke, Inc., Sunnyvale, CA … Cited by 1

A Complete Bibliography of ACM Transactions on Asian Language Information Processing NHF Beebe – 2014 – netlib.org … Kim:2003:RRE [37] Harksoo Kim and Jungyun Seo. Resolution of referring expressions in a Korean multimodal dialogue system. ACM Transactions on Asian Lan- guage Information Processing, 2(4):324–337, December 2003. CODEN ???? … Related articles All 7 versions

On the effective deployment of current machine translation technology J González Rubio – 2014 – riunet.upv.es … 83 3.4.2 Support Vector Machines . . … goal of NLP is to accomplish human-like language processing for a broad range of tasks or ap- plications, for instance information retrieval, information extraction, question answering, summarization, machine translation, dialog systems, etc. … Cited by 1 Related articles

Towards Modeling Collaborative Task Oriented Multimodal Human-human Dialogues L Chen – 2014 – indigo.uic.edu … xi Page 13. CHAPTER 1 INTRODUCTION A dialogue system is a computer system intended to converse with a human. The input … of communication. The most widely researched and developed dialogue systems are spoken dialogue sys- …

Speaker Profiling for Forensic Applications AH Poorjam – 2014 – researchgate.net … Mixture Model GS : Gaussian Scoring LM : Levenberg-Marquardt LR : Logistic Regression LSSVM : Least squares Support Vector Machines LSSVR : Least … profiling is also used in other applications such as improving service quality in dialog systems, categorizing large music … Cited by 1

Analytics for Power Grid Distribution Reliability in New York City C Rudin, S Ertekin, R Passonneau, A Radeva… – …, 2014 – pubsonline.informs.org … from which our system could learn (Passonneau et al. 2009). Currently, the system uses a support vector machines (Vapnik 1995) and a progressive clustering method (Xie et al. 2012) to label the tickets, based on features of … Cited by 1 Related articles All 4 versions

Latent semantic rational kernels for topic spotting on conversational speech C Weng, DL Thomson, P Haffner… – IEEE/ACM Transactions on …, 2014 – dl.acm.org … transducers (WFSTs). Classifica- tion can be conducted via support vector machine (SVM) using rational kernels based on WFSTs (lattices) which compactly represent all the most likely transcriptions from the ASR out- puts. This …

Text-To-Speech Synthesis F Hinterleitner, C Norrenbrock, S Möller, U Heute – Quality of Experience, 2014 – Springer … 13.2.3, even though the use case differs from the TTS databases investigated in most of the presented studies (short messages deployed, eg, in spoken dialogue systems), naturalness of voice and prosodic quality were also the main … support vector machines were implemented. … Related articles All 2 versions

Part-of-speech tagging in written slang V Korolainen – 2014 – jyx.jyu.fi … FIGURE 1: Architecture pipeline for a Spoken Dialogue System….. … Maximum-Entropy Hidden Markov Model NER Named Entity Recognition NLP Natural Language Processing NLTK Natural Language Toolkit POS Part-of-Speech SVM Support Vector Machines …

[BOOK] Natural Language Processing with Java and LingPipe Cookbook B Baldwin, K Dayanidhi – 2014 – books.google.com … Since first being introducedto the fieldin 2006,hehas worked ondiverse problems suchas spoken dialog systems, machine translation, text normalization, coreference resolution, and speechbased information retrieval, leadingto publications in esteemed conferences suchas …

Generative Probabilistic Models of Goal-Directed Users in Task-Oriented Dialogs A Eshky – homepages.inf.ed.ac.uk … 2014 Page 2. Page 3. Abstract A longstanding objective of human-computer interaction research is to develop better dialog systems for end users. The subset of user modelling research specifi- … design and improvement of dialog systems. Where dialog systems are commercially …

Text Analysis WS Bainbridge – Personality Capture and Emulation, 2014 – Springer … of innocuous email messages and telephone calls. More recently the villain acquired an accomplice, in industry’s campaign to replace human workers with computers in telephone dialog systems. NLP is such a vast field that … Related articles

[BOOK] Speech Processing in Mobile Environments KS Rao, AK Vuppala – 2014 – Springer … Some of the topics covered in this series include the presentation of real life commercial deployment of spoken dialog systems, contemporary methods of speech … A two-stage hybrid approach based on hidden Markov models (HMMs) and support vector machines (SVMs) is … Related articles All 3 versions

LRC Best Thesis Award Winner 2014 JG Rubio – localisation.ie … 83 3.4.2 Support Vector Machines . . … goal of NLP is to accomplish human-like language processing for a broad range of tasks or ap- plications, for instance information retrieval, information extraction, question answering, summarization, machine translation, dialog systems, etc. …

Robust Object Classification in Underwater Sidescan Sonar Images by Using Reliability-Aware Fusion of Shadow Features N Kumar, U Mitra, SS Narayanan – ieeexplore.ieee.org … recognition tasks [12], [13], where these models are used to identify out of vo- cabulary (OOV) words in dialog systems [14] or … B. Naive Feature Fusion Results First, we present results of naive feature fusion using pop- ular classifiers, viz., support vector machine (SVM) and …

Artificial Conversations for Chatter Bots Using Knowledge Representation, Learning, and Pragmatics C Chakrabarti – 2014 – repository.unm.edu … 22 2.2 The Syntactic Approach . . . . . 26 2.3 The Semantic Approach . . . . . 27 2.4 Dialogue systems . . . . . 27 viii Page 9. Contents 2.5 Limitations of existing approaches . . . . . 29 …

[BOOK] Social Interaction, Globalization and Computer-Aided Analysis: A Practical Guide to Developing Social Simulation A Osherenko – 2014 – books.google.com … 194 Appendix DA Sequential Dialog System Template . . . … Prisoner’s Dilemma Product Movement Correlation Coefficient Remote Management Agent Sensitive Artificial Listener Social Interaction Social Network Social Simulation Support Vector Machine WebService Unified …

Bidirectional optimization of the melting spinning process X Liang, Y Ding, Z Wang, K Hao, K Hone, H Wang – 2014 – ieeexplore.ieee.org Page 1. 240 IEEE TRANSACTIONS ON CYBERNETICS, VOL. 44, NO. 2, FEBRUARY 2014 Bidirectional Optimization of the Melting Spinning Process Xiao Liang, Yongsheng Ding, Senior Member, IEEE, Zidong Wang, Senior … Cited by 2 Related articles All 6 versions

Automated Grammatical Error Detection for Language Learners C Leacock, M Chodorow, M Gamon… – Synthesis Lectures on …, 2014 – morganclaypool.com … Semantic Role Labeling Martha Palmer, Daniel Gildea, and Nianwen Xue 2010 Spoken Dialogue Systems Kristiina Jokinen and Michael McTear 2009 Introduction to Chinese Natural Language Processing Kam-Fai Wong, Wenjie Li, Ruifeng Xu, and Zheng-sheng Zhang 2009 … Cited by 9 Related articles All 3 versions

[BOOK] Plan, Activity, and Intent Recognition: Theory and Practice G Sukthankar, C Geib, HH Bui, D Pynadath… – 2014 – books.google.com … 313 13 2 Related Work …..315 13 3 Rush Football …..317 13 4 Play Recognition Using Support Vector Machines …..319 13 5 Team … Cited by 3 Related articles

Developing an enriched natural language grammar for prosodically-improved concent-to-speech synthesis L Marais – 2014 – uir.unisa.ac.za Page 1. Developing an enriched natural language grammar for prosodically-improved concept-to-speech synthesis by Laurette Marais submitted in accordance with the requirements for the degree of MASTER OF SCIENCE in the subject of COMPUTING at the …

Predictive Spatial Search M Ali, M ABDELTAWAB, M DE COCK, A TEREDESAI – spatial.ucsb.edu … 1, p. 89, 2005. Page 12. 2014 Specialist Meeting—Spatial Search Conroy Dalton, Krukar—12 [5] H. Cuayáhuitl, N. Dethlefs, K.-F. Richter, T. Tenbrink, and J. Bateman, “A dialogue system for indoor wayfinding using text-based natural language,” Int. J. Comput. Linguist. Appl. …

[BOOK] Object-Oriented User Interfaces for Personalized Mobile Learning E Alepis, M Virvou – 2014 – Springer Page 1. Intelligent Systems Reference Library 64 Efthimios Alepis Maria Virvou Object-Oriented User Interfaces for Personalized Mobile Learning Page 2. Intelligent Systems Reference Library Volume 64 Series editors Janusz … Cited by 1 Related articles All 3 versions

Harmonic/percussive sound separation based on anisotropic smoothness of spectrograms H Tachibana, N Ono, H Kameoka… – IEEE/ACM Transactions …, 2014 – dl.acm.org … and Classification: Another approach is the fragmentation and classification, which firstly separates a spectrogram into many fragments, then unites some of these fragments using the estimated labels by a classification algo- rithm such as the support vector machine (SVM). …