RNN (Recurrent Neural Network) & Dialog Systems 2015


Notes:

From 2014 to 2015, the number of academic papers in Google Scholar covering recurrent neural networks and dialog systems tripled, from around 50x in 2014 to 150x in 2015.

  • dialog state tracker
  • dialog state tracking
  • discourse relation recognition

Resources:

  • ma3hmi.cogsy.de .. multimodal analyses enabling artificial agents in human-machine interaction (ma3hmi 2016)
  • sensei-conversation.eu .. making sense of human – human conversations (sensei fp7 project)
  • specom2016.hte.hu .. international conference on speech and computer (specom 2016)

See also:

100 Best Recurrent Neural Network VideosRNN (Recurrent Neural Network) & Dialog Systems 2014 | RNN (Recurrent Neural Network) & Question Answering Systems 2015


Using recurrent neural networks for slot filling in spoken language understanding G Mesnil, Y Dauphin, K Yao, Y Bengio… – … on Audio, Speech, …, 2015 – ieeexplore.ieee.org … In this study, we propose to use recurrent neural networks (RNNs) for this task, and present several novel architectures designed to efficiently model past and future temporal dependencies. … Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding … Cited by 60 Related articles All 16 versions

A neural network approach to context-sensitive generation of conversational responses A Sordoni, M Galley, M Auli, C Brockett, Y Ji… – arXiv preprint arXiv: …, 2015 – arxiv.org … To this end, we present two simple, context-sensitive response-generation models utilizing the Recurrent Neural Network Language Model (RLM … Unlike typical complex task-oriented multi-modular dialog systems (Young, 2002; Stent and Bangalore, 2014), our architecture is … Cited by 44 Related articles All 15 versions

Semantically conditioned lstm-based natural language generation for spoken dialogue systems TH Wen, M Gasic, N Mrksic, PH Su, D Vandyke… – arXiv preprint arXiv: …, 2015 – arxiv.org … The natural language generation (NLG) compo- nent provides much of the persona of a spoken dialogue system (SDS), and it has … Re- cent advances in recurrent neural network-based language models (RNNLM) (Mikolov et al., 2010; Mikolov et al., 2011a) have demonstrated … Cited by 47 Related articles All 18 versions

Multi-domain dialog state tracking using recurrent neural networks N Mrkši?, DO Séaghdha, B Thomson, M Gaši?… – arXiv preprint arXiv: …, 2015 – arxiv.org … of a dialog system is responsible for inter- preting the users’ utterances and thus updating the system’s belief state: a probability distribution over all possible states of the dialog. This belief state is used by the system to decide what to do next. Recurrent Neural Networks (RNNs … Cited by 12 Related articles All 16 versions

Stochastic language generation in dialogue using recurrent neural networks with convolutional sentence reranking TH Wen, M Gasic, D Kim, N Mrksic, PH Su… – arXiv preprint arXiv: …, 2015 – arxiv.org Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking … The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled … Cited by 16 Related articles All 19 versions

Learning from real users: Rating dialogue success with neural networks for reinforcement learning in spoken dialogue systems PH Su, D Vandyke, M Gasic, D Kim, N Mrksic… – arXiv preprint arXiv: …, 2015 – arxiv.org … S. Young, “Bayesian update of dialogue state: A pomdp framework for spoken dialogue systems.” Computer Speech and Language, vol. 24, pp. 562–588, 2010. [20] M. Lukoeviius and H. Jaeger, “Reservoir computing approaches to recurrent neural network training,” Computer … Cited by 10 Related articles All 12 versions

Neural responding machine for short-text conversation L Shang, Z Lu, H Li – arXiv preprint arXiv:1503.02364, 2015 – arxiv.org … spoken dialogue system. In Proceedings of the 2000 ANLP/NAACL Workshop on Conversational systems, pages 17–20. ACL. [Mikolov et al.2010] Tomas Mikolov, Martin Karafiát, Lukas Burget, Jan Cernock`y, and Sanjeev Khudanpur. 2010. Recurrent neural network based … Cited by 53 Related articles All 12 versions

The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems R Lowe, N Pow, I Serban, J Pineau – arXiv preprint arXiv:1506.08909, 2015 – arxiv.org … more specifically with neural architec- tures [1]; however, it is worth noting that many of the most successful approaches, in particular convolutional and recurrent neural networks, were known … Similar progress has not yet been observed in the development of dialogue systems. … Cited by 17 Related articles All 12 versions

A critical review of recurrent neural networks for sequence learning ZC Lipton, J Berkowitz, C Elkan – arXiv preprint arXiv:1506.00019, 2015 – arxiv.org … Besides dialogue systems, modern interactive systems of economic importance include self-driving cars and robotic surgery, among others. … 1.2 Why not use Markov models? Recurrent neural networks are not the only models capable of representing time dependencies. … Cited by 22 Related articles All 10 versions

Hierarchical neural network generative models for movie dialogues IV Serban, A Sordoni, Y Bengio, A Courville… – arXiv preprint arXiv: …, 2015 – arxiv.org … They are close to human spoken language (Forchini, 2009), which makes them suitable for bootstrapping goal-driven dialogue systems. … By means of such distributed representations, the recurrent neural network (RNN) based language model (Mikolov et al., 2010) has pushed … Cited by 16 Related articles All 4 versions

Is it time to switch to Word Embedding and Recurrent Neural Networks for Spoken Language Understanding? V Vukotic, C Raymond, G Gravier – InterSpeech, 2015 – hal.inria.fr … Neural Net- works, especially Recurrent Neural Networks, that are specifi- cally adapted to sequence labeling problems, have been applied successfully … MEDIA The research project MEDIA [8] evaluates different SLU mod- els of spoken dialogue systems dedicated to provide … Cited by 12 Related articles All 11 versions

A regression approach to speech enhancement based on deep neural networks Y Xu, J Du, LR Dai, CH Lee – IEEE/ACM Transactions on Audio, …, 2015 – ieeexplore.ieee.org … Deep recurrent neural networks (DRNNs) were also adopted in the feature enhancement for robust speech recognition [24, 25]. The generalization capacity of the DRNN was weak if it was trained on limited noise types [24]. … Cited by 60 Related articles All 2 versions

Contextual spoken language understanding using recurrent neural networks Y Shi, K Yao, H Chen, YC Pan… – … on Acoustics, Speech …, 2015 – ieeexplore.ieee.org … Neural Networks, Convolution Net- works, Spoken Language Understanding 1. INTRODUCTION A Spoken Language Understanding (SLU) system consists of domain identification, intent classification and slot filling [1]. SLU is a crit- ical component in spoken dialogue systems. … Cited by 11 Related articles All 7 versions

Attention with Intention for a Neural Network Conversation Model K Yao, G Zweig, B Peng – arXiv preprint arXiv:1510.08565, 2015 – arxiv.org … [15] T.-H. Wen, M. Gasic, D. Kim, N. Mrksic, P.-H. Su, D. Vandyke, and S. Young. Stochastic language generation in dialogue using recurrent neural networks with convoulutional sentence reranking. … POMDP-based statistical spoken dialog systems: A review. … Cited by 8 Related articles All 3 versions

Reward shaping with recurrent neural networks for speeding up on-line policy learning in spoken dialogue systems PH Su, D Vandyke, M Gasic, N Mrksic, TH Wen… – arXiv preprint arXiv: …, 2015 – arxiv.org Abstract: Statistical spoken dialogue systems have the attractive property of being able to be optimised from data via interactions with real users. However in the reinforcement learning paradigm the dialogue manager (agent) often requires significant time to explore the state … Cited by 2 Related articles All 18 versions

Advances in natural language processing J Hirschberg, CD Manning – Science, 2015 – science.sciencemag.org … Today’s researchers refine and make use of such tools in real-world applications, creating spoken dialogue systems and speech-to … For translation, research has focused on a particular version of recurrent neural networks, with enhanced “long short-term memory” computational … Cited by 21 Related articles All 9 versions

Improved deep learning baselines for ubuntu corpus dialogs R Kadlec, M Schmid, J Kleindienst – arXiv preprint arXiv:1510.03753, 2015 – arxiv.org … References [1] R. Lowe, N. Pow, I. Serban, and J. Pineau, “The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems,” arXiv preprint arXiv … [6] M. Schuster and KK Paliwal, “Bidirectional recurrent neural networks,” IEEE Transactions on … Cited by 6 Related articles All 3 versions

Video paragraph captioning using hierarchical recurrent neural networks H Yu, J Wang, Z Huang, Y Yang, W Xu – arXiv preprint arXiv:1510.07712, 2015 – arxiv.org Page 1. Video Paragraph Captioning using Hierarchical Recurrent Neural Networks Haonan Yu1? Jiang Wang2 Zhiheng Huang2 Yi Yang2 Wei Xu2 1Purdue University haonan@haonanyu.com 2Baidu Research – Institute of Deep Learning … Cited by 7 Related articles All 5 versions

Evaluating prerequisite qualities for learning end-to-end dialog systems J Dodge, A Gane, X Zhang, A Bordes, S Chopra… – arXiv preprint arXiv: …, 2015 – arxiv.org … 1 INTRODUCTION With the recent employment of Recurrent Neural Networks (RNNs) and the large quantities of con- versational data available on websites like Twitter or Reddit, a new type of dialog system is emerg- ing. Such … Cited by 10 Related articles All 3 versions

Incremental recurrent neural network dependency parser with search-based discriminative training M Yazdani, J Henderson – 2015 – archive-ouverte.unige.ch … Page 3. est for inducing vector representations of complex linguistic structures. 1.2 Incremental Recurrent Neural Network Architecture … It also easily supports incremental in- terpretation in dialogue systems, or incremental language modeling for speech recognition. … Cited by 6 Related articles All 9 versions

A survey of available corpora for building data-driven dialogue systems IV Serban, R Lowe, L Charlin, J Pineau – arXiv preprint arXiv:1512.05742, 2015 – arxiv.org … where at is the dialogue system response action at time t, and ? is the set of parameters which defines f. Rule-based systems … or combined models, such as the model built on both a phrase-based statistical machine translation system and a recurrent neural network proposed … Cited by 8 Related articles All 2 versions

Recurrent neural networks for incremental disfluency detection J Hough, D Schlangen – Proceedings of Interspeech, 2015 – dsg-bielefeld.de … For dialogue systems to become robust, they must be able to de- tect disfluencies accurately and with minimal latency. … tagging task and, following their recent suc- cess in Spoken Language Understanding tasks, we test the per- formance of Recurrent Neural Networks (RNNs). … Cited by 3 Related articles All 5 versions

Recurrent neural network and LSTM models for lexical utterance classification S Ravuri, A Stolcke – Proc. Interspeech, Dresden, 2015 – research-srv.microsoft.com … [2] E. Shriberg, A. Stolcke, and S. Ravuri, “Addressee de- tection for dialog systems using temporal and spectral di- mensions of speaking style”, in Proc. … 1997. [11] T. Mikolov, M. Karafiát, L. Burget, JH ?Cernocký, and S. Khudanpur, “Recurrent neural network based language … Cited by 8 Related articles All 5 versions

Incremental LSTM-based dialog state tracker L Zilka, F Jurcicek – 2015 IEEE Workshop on Automatic Speech …, 2015 – ieeexplore.ieee.org … and model averaging. Index Terms— spoken dialog systems, dialog state track- ing, recurrent neural networks, LSTM 1. INTRODUCTION A dialog state tracker is an important component of statistical spoken dialog systems. … Cited by 6 Related articles All 4 versions

Efficient learning for spoken language understanding tasks with word embedding based pre-training Y Luan, S Watanabe, B Harsham – Sixteenth Annual Conference of the …, 2015 – Citeseer … SLU) tasks such as goal esti- mation and intention identification from user’s commands are essential components in spoken dialog systems. … In order to capture long-term characteristics over the entire dialog, we propose a novel Recurrent Neural Network (RNN) architecture. … Cited by 4 Related articles All 8 versions

Applying deep learning to answer selection: A study and an open task M Feng, B Xiang, MR Glass, L Wang… – 2015 IEEE Workshop …, 2015 – ieeexplore.ieee.org … ken Question Answering System 1. INTRODUCTION Natural language understanding based spoken dialog system has been a popular topic in the past years of artificial intelli- gence renaissance. Many of those influential systems … Cited by 12 Related articles All 4 versions

Recurrent polynomial network for dialogue state tracking K Sun, Q Xie, K Yu – arXiv preprint arXiv:1507.03934, 2015 – arxiv.org … Figure 1: Diagram of a spoken dialogue system (SDS) … statistical models, such as Maximum Entropy (MaxEnt) (Lee and Eskenazi, 2013), Conditional Random Field (Lee, 2013), Deep Neural Network (DNN) (Sun et al., 2014b), and Recurrent Neural Network (RNN) (Hender- son … Cited by 4 Related articles All 5 versions

Discriminative methods for statistical spoken dialogue systems MS Henderson – 2015 – repository.cam.ac.uk … formation from computer systems. Statistical spoken dialogue systems are able to disam- … speech recognition results to the dialogue state without using an explicit semantic decoder. The method is based on a recurrent neural network structure that is capable of generalis- … Cited by 4 Related articles All 3 versions

Fix it where it fails: Pronunciation learning by mining error corrections from speech logs Z Kou, D Stanton, F Peng, F Beaufays… – … on Acoustics, Speech …, 2015 – ieeexplore.ieee.org … Sak, and Françoise Bea- ufays, “Grapheme-to-phoneme conversion using long short- term memory recurrent neural networks,” in Proceedings of … Franco, “Using dialog corrections to improve speech recognition,” in Error Handling in Spoken Language Dialogue Systems, 2003. … Cited by 5 Related articles All 6 versions

Listen, attend, and walk: Neural mapping of navigational instructions to action sequences H Mei, M Bansal, MR Walter – arXiv preprint arXiv:1506.04089, 2015 – arxiv.org … We propose a recurrent neural network with long short- term memory (LSTM) (Hochreiter and Schmidhuber 1997) to both encode the navigational instruction sequence bidi- rectionally and to decode the representation to an action sequence, based on a representation of the … Cited by 6 Related articles All 8 versions

How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary? F Huszár – arXiv preprint arXiv:1511.05101, 2015 – arxiv.org … This narrower definition extends to use-cases such as image captioning, texture generation, machine translation and dialogue systems, but excludes … In recurrent neural network (RNN) terminology, this would means that the optimal architecture under SS uses its hidden states … Cited by 6 Related articles All 3 versions

Machine learning for dialog state tracking: A review M Henderson – 2015 – research.google.com … Early spoken dialog systems used hand-crafted rules for DST, keep- ing a single top hypothesis for each component of the dialog state … Recurrent Neural Networks (RNNs) have been proposed as se- quential models that are able to deal with high dimensional continu- ous input … Cited by 3 Related articles All 2 versions

Multi-domain dialogue success classifiers for policy training D Vandyke, PH Su, M Gasic, N Mrksic… – … IEEE Workshop on …, 2015 – ieeexplore.ieee.org … Gašic, Pei-Hao Su, David Vandyke, Tsung-Hsien Wen, and Steve Young, “Multi-domain dialog state tracking using recurrent neural networks.,” in ACL, 2015. [29] Diane J. Litman and Shimei Pan, “Designing and eval- uating an adaptive spoken dialogue system,” User Mod … Cited by 2 Related articles All 8 versions

Word embeddings combination and neural networks for robustness in asr error detection S Ghannay, Y Esteve, N Camelin – … (EUSIPCO), 2015 23rd …, 2015 – ieeexplore.ieee.org … [4] Tam Yik-Cheung, Yun Lei, Jing Zheng, and Wen Wang, “ASR error detection using recurrent neural network … of word and semantic features for theme identification in telephone conversations,” in 6th International Work- shop on Spoken Dialog Systems (IWSDS 2015), 2015. … Cited by 5 Related articles All 3 versions

A cognitive neural architecture able to learn and communicate through natural language B Golosio, A Cangelosi, O Gamotina, GL Masala – PloS one, 2015 – journals.plos.org … Recently, deep learning techniques based on recurrent neural networks (RNNs) have been used successfully for several NLP tasks, including speech recognition [14], parsing [15,16], machine translation [17], sentiment analysis of text [18]. … Cited by 6 Related articles All 17 versions

Deep Contextual Language Understanding in Spoken Dialogue Systems C Liu, P Xu, R Sarikaya – Sixteenth Annual Conference of …, 2015 – research.microsoft.com … The dialog system responses beyond SLU component are also exploited as effective external features. … Index Terms: convolutional neural networks, recurrent neural networks, spoken language understanding 1. Introduction … Cited by 2 Related articles All 4 versions

Spoken language understanding in a nutrition dialogue system MB Korpusik – 2015 – dspace.mit.edu Page 1. Spoken Language Understanding in a Nutrition ARCVES Dialogue System by Mandy B. Korpusik … the lack of a specific nutrient. 1.1 Dialogue Systems Spoken dialogue systems like this one have become increasingly prevalent in today’s … Cited by 3 Related articles All 3 versions

Modeling phrasing and prominence using deep recurrent learning A Rosenberg, R Fernandez… – … Annual Conference of …, 2015 – researchgate.net … [17] K. Laskowski, J. Edlund, and M. Heldner, “An instantaneous vec- tor representation of delta pitch for speaker-change prediction in conversational dialogue systems,” in ICASSP … [21] A. Graves, Supervised Sequence Labelling with Recurrent Neural Networks. Springer, 2012. … Cited by 6 Related articles All 2 versions

Yarbus: Yet another rule based belief update system J Fix, H Frezza-Buet – arXiv preprint arXiv:1507.06837, 2015 – arxiv.org … As shown by the authors, the recurrent neural network performs well on the dataset and their sensitivity to the history of the inputs certainly … of the last two methods is their ability to solve the belief tracking problem without requiring much of expert knowledge in dialog systems. … Cited by 2 Related articles All 3 versions

LecTrack: Incremental Dialog State Tracking with Long Short-Term Memory Networks L Žilka, F Jur?í?ek – International Conference on Text, Speech, and …, 2015 – Springer … trainable from annotated data. The tracker achieves promising performance on the Method and Requested tracking sub-tasks in DSTC2. Keywords: Dialog systems · Recurrent neural network · Dialog state tracking 1 Introduction … Cited by 1 Related articles

A comparative study of neural network models for lexical intent classification S Ravuri, A Stoicke – 2015 IEEE Workshop on Automatic …, 2015 – ieeexplore.ieee.org … Utterance classification is an important pre-processing step for many dialog systems that interpret speech input. … detection, in which a system must identify whether speech is directed at the machine, or another human, and recently, we compared recurrent neural network … Cited by 1 Related articles All 4 versions

Recurrent Polynomial Network for Dialogue State Tracking with Mismatched Semantic Parsers Q Xie, K Sun, S Zhu, L Chen… – 16th Annual Meeting of …, 2015 – anthology.aclweb.org … t are evaluated using the nodes’ values at time t and the nodes’ values at time t? 1 as inputs just like Recurrent Neural Networks (RNNs). … the briefness and effectiveness of the simple structure shown in figure 2. 4 Uncertainty in SLU In an end-to-end dialogue system, there are … Cited by 1 Related articles All 10 versions

Which ASR errors are hard to detect S Ghannay, N Camelin, Y Esteve – Errors by Humans and …, 2015 – errare2015.racai.ro … [4] T. Yik-Cheung, Y. Lei, J. Zheng, and W. Wang, “ASR error de- tection using recurrent neural network language model … of word and semantic features for theme identification in telephone conversa- tions,” in 6th International Workshop on Spoken Dialog Systems (IWSDS 2015 … Cited by 2 Related articles All 2 versions

Recurrent Reinforcement Learning: A Hybrid Approach X Li, L Li, J Gao, X He, J Chen, L Deng, J He – arXiv preprint arXiv: …, 2015 – arxiv.org … follows: • First, unlike Mnih et al. (2015), we employ recurrent neural networks (RNN) and long short-term memory (LSTM) (Hochreiter and Schmidhuber 1997) models to learn the representation of states for RL. Since these … Cited by 3 Related articles All 3 versions

Multitask learning of deep neural networks for low-resource speech recognition D Chen, BKW Mak – IEEE/ACM Transactions on Audio, Speech, …, 2015 – ieeexplore.ieee.org … for several language processing predictions; [38] improves intent classification in goal-oriented human-machine spoken dialog systems which is … in ASR Using DNNs In ASR, MTL has been applied to improving performance robustness using recurrent neural networks [40]. … Cited by 3 Related articles All 4 versions

Constrained markov bayesian polynomial for efficient dialogue state tracking K Yu, K Sun, L Chen, S Zhu – IEEE/ACM Transactions on Audio, …, 2015 – ieeexplore.ieee.org … used for spoken dialogue systems [7]. The results of the DSTC [5] further demonstrate the power of discriminative statistical models, such as Maximum Entropy [8], Conditional Random Field [9], Deep Neural Net- work (DNN) [10], and Recurrent Neural Network (RNN) [11]. … Cited by 4 Related articles All 4 versions

Learning to understand phrases by embedding the dictionary F Hill, K Cho, A Korhonen, Y Bengio – arXiv preprint arXiv:1504.00548, 2015 – arxiv.org … 2 Neural Language Model Architectures The first model we apply to the dictionary-based learning task is a recurrent neural network (RNN). RNNs operate on variable-length sequences of in- puts; in our case, natural language definitions, descriptions or sentences. … Cited by 12 Related articles All 10 versions

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, 2015 – Springer … As a result a novel sentiment analysis model is pro- posed based on recurrent neural network, which takes the par- tial document as input and then the next parts to predict the sentiment label distribution rather than the next word. … 2.2 Recurrent neural network … Cited by 2 Related articles All 4 versions

Detecting repetitions in spoken dialogue systems using phonetic distances J Lopes, G Salvi, G Skantze, A Abad… – … Annual Conference of …, 2015 – inesc-id.pt … Two different approaches have been used to built classifiers for detecting repetitions in spoken dialogue system data. … The phonetic posteriors for the SweCC data were estimated with a Recurrent Neural Network (RNN) described in [13], trained with the Swedish SpeechDat … Cited by 1 Related articles All 7 versions

Learning Domain-Independent Dialogue Policies via Ontology Parameterisation Z Wang, TH Wen, PH Su, Y Stylianou – … of the Special Interest Group on …, 2015 – aclweb.org … 2015. Multi- domain dialog state tracking using recurrent neural networks. In ACL-IJCNLP 2015. … 2010. Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems. Computer Speech and Language, 24 (4): 562–588. Zhuoran Wang and Oliver Lemon. … Cited by 2 Related articles All 16 versions

Towards universal paraphrastic sentence embeddings J Wieting, M Bansal, K Gimpel, K Livescu – arXiv preprint arXiv: …, 2015 – arxiv.org … We present the interesting result that simple com- positional architectures based on updated vector averaging vastly outperform long short-term memory (LSTM) recurrent neural networks and that these simpler ar- chitectures allow us to learn models with superior generalization … Cited by 13 Related articles All 2 versions

Multimodal Analyses enabling Artificial Agents in Human-Machine Interaction R Böck, F Bonin, N Campbell, R Poppe – 2015 – Springer … Gestures….. 57 Ronald Böck, Kirsten Bergmann, and Petra Jaecks Dialogs and Speech Recognition ASR Independent Hybrid Recurrent Neural Network Based Error Correction for Dialog System Applications….. 69 … Cited by 1 Related articles

User behavior fusion in dialog management with multi-modal history cues M Yang, J Tao, L Chao, H Li, D Zhang, H Che… – Multimedia Tools and …, 2015 – Springer … effective multi-modal behavior fusion model and flexible behavior sensitive DM are necessary for practical human computer dialog systems. … It adopts recurrent neural network architectures which take into account past observations by cyclic connections in the network’s hidden … Cited by 1 Related articles All 6 versions

Automatic Speech Recognition-A Literature Survey on Indian languages and Ground Work for Isolated Kannada Digit Recognition using MFCC and ANN SB Harisha, S Amarappa, DSV Sathyanarayana – International Journal of … – eslibrary.org … (2012) [13] proposed a prototype Speech to Text Conversion System (STSC) using LPC and Recurrent Neural Network (RNN). … (2006) [108] presented an inexpensive approach for gathering the linguistic resources needed to power a simple spoken dialog system. … Cited by 2 Related articles All 2 versions

ASR Independent Hybrid Recurrent Neural Network Based Error Correction for Dialog System Applications J Bang, S Park, GG Lee – Multimodal Analyses enabling Artificial …, 2015 – books.google.com Abstract. We proposed an automatic speech recognition (ASR) error correction method using hybrid word sequence matching and recurrent neural network for dialog system applications. Basically, the ASR errors are corrected by the word sequence matching … Related articles

Multi-agent learning in multi-domain spoken dialogue systems M Gašic, N Mrkšic, LR Barahona, PH Su, D Vandyke… – pdfs.semanticscholar.org … Previous work on multi-domain dialogue systems has proposed a distributed architecture where a generic policy can be trained on data coming from dif … based set-up, with subjects recruited via Amazon MTurk, in the same set up as in [11]. A recurrent neural network model was … Related articles All 3 versions

Dialogue Management based on Sentence Clustering W Ge, B Xu – Volume 2: Short Papers – aclweb.org … provides an overview of the current state of the art in the development of POMDP-based spo- ken dialog systems.(Hao et … words and sentence structures and external information such as the dis- tributed representation of sentence (vector) from Recurrent Neural Networks (RNN … Related articles All 7 versions

A language model based approach towards large scale and lightweight language identification systems BML Srivastava, HK Vydana, AK Vuppala… – arXiv preprint arXiv: …, 2015 – arxiv.org … ABSTRACT Multilingual spoken dialogue systems have gained promi- nence in the recent past necessitating the requirement for … discriminative phonotactic information in the obtained phone sequences are modeled using statistical and recurrent neural network based language … Related articles All 3 versions

Dialogue State Tracking using Long Short Term Memory Neural Networks K Yoshino, T Hiraoka, G Neubig, S Nakamura – phontron.com … change expands the variety of expressions of users, because the users will be free from limitations im- posed by dialogue systems. … Previously, recurrent neural networks (RNNs) have been used for the dialogue state tracking [2, 1] and they achieved good results in previous … Related articles

Toward Multi-domain Language Generation using Recurrent Neural Networks TH Wen, M Gašic, N Mrkšic, LM Rojas-Barahona… – svr-ftp.eng.cam.ac.uk … Pomdp-based statistical spoken dialog systems: A review. Proceedings of the IEEE, 2013. [2] Matthew Henderson, Blaise Thomson, and Steve Young. Proceedings of SIGdial, chapter Word-Based Dialog State Tracking with Recurrent Neural Networks. … Related articles All 7 versions

The MSIIP System for Dialog State Tracking Challenge 4 M Li, J Wu – colips.org … [10] Matthew Henderson, Blaise Thomson, and Steve Young. Word-based dialog state tracking with recurrent neural networks. … A probabilistic framework for repre- senting dialog systems and entropy-based dialog management through dy- namic stochastic state evolution. …

Natural Language Dialogue-Future Way of Accessing Information H Li – 2015 – hangli-hl.com … or word2vec W M Page 34. Recurrent Neural Network (RNN) (Mikolov et al. 2010) the cat sat …. mat … Neural Responding Machine (Shang et al., ACL 2015) Page 40. Natural Language Dialogue System – Retrieval based Approach index of messages and responses … Related articles All 2 versions

Recurrent Neural Networks in Speech Disfluency Detection and Punctuation Prediction M Reisser – 2015 – isl.anthropomatik.kit.edu … “This thesis is uhm I mean discusses neural networks uh recurrent neural networks in modelling … that understand spoken language and are able to perform natural language processing (NLP) tasks such as automated translation, information extraction or dialogue systems, is a …

Context Sensitive Spoken Language Understanding using Role Dependent LSTM layers H Chiori, T Hori, S Watanabe, JR Hershey – 2015 – pdfs.semanticscholar.org … Teruhisa Misu, Hideki Kashioka, and Satoshi Nakamura, “Sta- tistical dialog management applied to WFST-based dialog systems,” in IEEE … Yi-Cheng Pan, Mei-Yuh Hwang, and Baolin Peng, “Contextual spoken language understanding using recurrent neural networks,” in IEEE … Cited by 1 Related articles All 3 versions

An Auto-Encoder for Learning Conversation Representation Using LSTM X Zhou, B Hu, Q Chen, X Wang – International Conference on Neural …, 2015 – Springer … Linguist. 26(3), 339–374 (2000)CrossRef. 4. Rieser, V., Lemon, O.: Natural language generation as planning under uncertainty for spoken dialogue systems. In: Proceedings of EACL, pp. … 64–72 (2010). 6. Graves, A.: Generating sequences with recurrent neural networks. … Related articles

[BOOK] Multimodal Analyses enabling Artificial Agents in Human-Machine Interaction: Second International Workshop, MA3HMI 2014, Held in Conjunction with … R Böck, F Bonin, N Campbell, R Poppe – 2015 – books.google.com … Gestures….. Ronald Böck, Kirsten Bergmann, and Petra Jaecks Dialogs and Speech Recognition ASR Independent Hybrid Recurrent Neural Network Based Error Correction for Dialog System Applications….. Junhwi … All 2 versions

Dialog Management with Deep Neural Networks L Zilka – pdfs.semanticscholar.org … Our approach is based on the modern deep learning techinques, particularly the long-short term memory recurrent neural network (LSTM RNN … of the sequences successfully modelled by LSTMs is comparable to the length of the word sequences in the spoken dialog systems. … Related articles All 3 versions

Investigation of ensemble models for sequence learning A Celikyilmaz, D Hakkani-Tur – 2015 IEEE International …, 2015 – ieeexplore.ieee.org … (4) RNNSEQ: Recurrent Neural Network (RNN) for se … sets, and ‘testa’ for testing. For SLU, we use a dataset of utterances from real- use scenarios of a spoken dialog system. The utterances are from domains of audiovisual media, including movies, music, games, tv shows. … Related articles All 8 versions

Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study M Sheikhan, M Abbasnezhad Arabi… – Connection …, 2015 – Taylor & Francis … Enhancement of emotion detection in spoken dialogue systems by combining several information sources. … With the aim of selecting more efficient features and reducing the number of input features to the recurrent neural network (RNN), the sequential forward feature selection … Related articles All 3 versions

Convolutional Neural Networks for Multi-topic Dialog State Tracking H Shi, T Ushio, M Endo, K Yamagami, N Horii – colips.org … A more recent related work on multi-domain dialog state tracking using recurrent neural networks (RNNs) was proposed by Mrkšic et al. [7]. Their idea for domain Page 4. … Data-Driven Methods for Adaptive Spoken Dialogue Systems. Springer, 2012. …

Self-Configuring Ensemble of Neural Network Classifiers for Emotion Recognition in the Intelligent Human-Machine Interaction E Sopov, I Ivanov – Computational Intelligence, 2015 IEEE …, 2015 – ieeexplore.ieee.org … Much research has been done on building intelligent dialogue systems (DS) that are able to collect this kind of information. … al. in [7] proposed a hybrid multi-objective evolutionary algorithm for optimizing the structure of recurrent neural networks for time series prediction. … Related articles

Driver prediction to improve interaction with in-vehicle HMI B Harsham, S Watanabe, A Esenther, J Hershey… – 2015 – merl.com … prediction through 1) the driver interaction with the car HMI based on the driving history and 2) in-vehicle dialog systems based on … The second to- pic improved the prediction performance of user intention and goal estimation tasks based on a novel recurrent neural network. … Cited by 1 Related articles All 6 versions

Discourse Relation Recognition by Comparing Various Units of Sentence Expression with Recursive Neural Network A Otsuka, T Hirano, C Miyazaki, R Masumura… – 2015 – aclweb.org … Current dialogue systems have problems that they choose a contextually inappropriate utterance for the user in- put. … Shujie Liu, Nan Yang, Mu Li, and Ming Zhou. 2014. A recursive recurrent neural network for statistical ma- chine translation. … Related articles All 8 versions

Hybrid Dialog State Tracker M Vodolán, R Kadlec, J Kleindienst – arXiv preprint arXiv:1510.03710, 2015 – arxiv.org … It abstracts away the subsystems of end-to-end spoken dialog systems, focusing only on the dialog state tracking. … The core of our proposed tracker consists of several update rules that use a few parameters that are computed by a recurrent neural network. … Cited by 2 Related articles All 3 versions

Learning to Process Natural Language in Big Data Environment H Li – 2015 – hangli-hl.com … al., ACL-IJCNLP 2015) Page 33. Natural Language Dialogue System – Retrieval based Approach index of messages and responses matching ranking … responses Page 34. Retrieval based Dialogue System (Ji et al., 2014) • Matching Models (Features) –Deep Match CNN … Related articles All 3 versions

Dialogue Management based on Multi-domain Corpus W Ge, B Xu – 16th Annual Meeting of the Special Interest …, 2015 – anthology.aclweb.org … ac. cn Abstract Dialogue Management (DM) is a key is- sue in Spoken Dialogue System. … (Thomson, 2010) introduces a new POMDP-based framework for building spoken dialogue systems by using Bayesian updates of the dialogue state. … Related articles All 9 versions

Emotion recognition in spontaneous and acted dialogues L Tian, JD Moore, C Lai – Affective Computing and Intelligent …, 2015 – ieeexplore.ieee.org … In a virtual agent dialogue system, the ability to recognize and express emotions can make the agent appear more natural and … the performance of the widely used Support Vector Machines (SVM) and the Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) as … Cited by 1 Related articles All 10 versions

Structured Vectors for Chinese Word Representations C Li, B Xu, X Wang, G Wu, G Tian… – International Journal of …, 2015 – search.proquest.com … [25] T. Mikolov and G. Zweig, Context dependent recurrent neural network language model, SLT, 2012. … His current research interests include spoken dialogue systems, dialogue management, reinforcement learning and deep learning. … Related articles All 3 versions

YARBUS: Yet Another Rule Based belief Update System Jérémy Fix Hervé Frezza-Buet J Fix – 2015 – researchgate.net … As shown by the authors, the recurrent neural network performs well on the dataset and their sensitivity to the history of the inputs certainly … of the last two methods is their ability to solve the belief tracking problem without requiring much of expert knowledge in dialog systems. … Related articles All 5 versions

Recognizing emotions in dialogues with acoustic and lexical features L Tian, JD Moore, C Lai – Affective Computing and Intelligent …, 2015 – ieeexplore.ieee.org … Descriptors (LLD) [21] • Model: ? Contextual: Non-contextual:Support Vector Machine (SVM) Contextual: Long Short-Term Memory Recurrent Neural Network (LSTM) ? Structural … We plan to work on this question in the future if we have an available dialogue system to apply our … Related articles All 6 versions

Lexical embedding adaptation for open-domain spoken language understanding J Tafforeau, F Bechet, B Favre, T Artieres – researchgate.net … aspx?id=164624 [4] G. Mesnil, X. He, L. Deng, and Y. Bengio, “Investigation of recurrent-neural- network archi- tectures … P. Pasupat, and R. Sarikaya, “Enriching word embeddings using knowledge graph for semantic tagging in conversational dialog systems.” AAAI – Association … Related articles All 2 versions

Weakly Supervised Natural Language Processing Framework for Abstractive Multi-Document Summarization: Weakly Supervised Abstractive Multi-Document … P Li, W Cai, H Huang – Proceedings of the 24th ACM International on …, 2015 – dl.acm.org … After that, our system generates new patterns by fusing existing patterns and selecting top ranked new patterns via the recurrent neural network language model. … Keywords Summarization; Recurrent Neural Network; Capped Norm Semi- Supervised Learning … Related articles

[BOOK] Speech and Computer: 17th International Conference, SPECOM 2015, Athens, Greece, September 20-24, 2015, Proceedings A Ronzhin, R Potapova, N Fakotakis – 2015 – books.google.com … Andrey Ronzhin, and Alexander Ronzhin Analysing Human-Human Negotiations with the Aim to Develop a Dialogue System….. … 333 Gerasimos Arvanitis, Konstantinos Moustakas, and Nikos Fakotakis Recurrent Neural Networks for Hypotheses Re-Scoring …

Labeling Sequential Data Based on Word Representations and Conditional Random Fields X Wang, B Xu, C Li, W Ge – International Journal of Machine …, 2015 – search.proquest.com … for word representation such as (SENNA) [24], hierarchical log-bilinear (HLBL) [25] and recurrent neural network based language … Her research interests include pattern recognition, neural networks, machine learning, natural language processing, and spoken dialog systems. … Related articles All 3 versions

Hyper-parameter Optimisation of Gaussian Process Reinforcement Learning for Statistical Dialogue Management L Chen, PH Su, M Gašic – 16th Annual Meeting of the Special …, 2015 – anthology.aclweb.org … References Paul A Crook and Oliver Lemon. 2011. Lossless value directed compression of complex user goal states for statistical spoken dialogue systems. In INTER- SPEECH, pages 1029–1032. … 2014. Word-based dialog state tracking with recurrent neural networks. … All 11 versions

Recurrent Models for Auditory Attention in Multi-Microphone Distance Speech Recognition S Kim, I Lane – arXiv preprint arXiv:1511.06407, 2015 – arxiv.org … We embed an attention mechanism within a Recurrent Neural Network (RNN) based acoustic model to automatically tune its attention to a … Many real-world speech recognition applications, including teleconferencing, robotics and in-car spoken dialog systems, must deal with … Cited by 1 Related articles All 3 versions

Deep Neural Networks in Speech Recognition AL Maas – 2015 – stacks.stanford.edu … 101 6.1 Deep bi-directional recurrent neural network to map input audio fea- tures X to a distribution ppc|xtqover output characters at each timestep … whether to call or email that contact. In a more complex dialog system, we may wish … Related articles

Semi-supervised slot tagging in spoken language understanding using recurrent transductive support vector machines Y Shi, K Yao, H Chen, YC Pan… – 2015 IEEE Workshop on …, 2015 – ieeexplore.ieee.org … Microsoft 1. INTRODUCTION Recurrent neural network (RNN) and its variants [6, 7, 8, 9, 10, 11] are the cutting-edge techniques for slot tagging. … Index Terms- Recurrent neural networks, transductive support vector machines, semi-supervised learning … Related articles

Comparison of Emotion Recognition Models in Spoken Dialogs P Garg, S Sehgal – extraction, 2015 – ijournals.in … An emotion is feelings with which a human speak, perceive and communicate with others but a machine dialog system always perform all … Almost all ANNs can be categorized into three main basic types: MLP, recurrent neural networks (RNN), and radial basis functions (RBF … Related articles

Topics, Trends, and Resources in Natural Language Processing (NLP) M Bansal – Citeseer Page 1. Topics, Trends, and Resources in Natural Language Processing (NLP) Mohit Bansal TTI-Chicago (CSC2523, ‘Visual Recognition with Text’, UToronto, Winter 2015 – 01/21/2015) (various slides adapted/borrowed from Dan Klein’s and Chris Manning’s course slides) … Related articles All 2 versions

The SENSEI Project: Making Sense of Human Conversations G Riccardi, F Bechet, M Danieli, B Favre… – … Workshop on Future …, 2015 – Springer … It is reminiscent of the recent trend towards conditioned language models [27, 31] which use Recurrent Neural Networks for producing words. A similar approach [15] finds sentence communities through textual entailment and merges them. … Related articles All 2 versions

Maximum-likelihood normalization of features increases the robustness of neural-based spoken human-computer interaction E Trentin – Pattern Recognition Letters, 2015 – Elsevier Robust acoustic modeling is essential in the development of automatic speech recognition systems applied to spoken human-computer interaction. To this end, trad. Related articles All 3 versions

Unsupervised Learning and Modeling of Knowledge and Intent for Spoken Dialogue Systems YN Chen – target, 2015 – cs.cmu.edu Page 1. Unsupervised Learning and Modeling of Knowledge and Intent for Spoken Dialogue Systems Yun-Nung (Vivian) Chen Ph.D. Thesis Proposal … 69 xiii Page 20. xiv Page 21. 1Introduction 1.1 Spoken Dialogue System … Related articles All 10 versions

Speech Recognition in Indian Languages—A Survey M Sarma, KK Sarma – Recent Trends in Intelligent and Emerging Systems, 2015 – Springer … where a spoken dialogue system is designed to use in agricultural commodities task domain using real-world speech data … The problem of speech recognition inevitably requires handling of temporal variation and ANN architecture like recurrent neural network (RNN), time … Related articles All 5 versions

Multi-task Learning Deep Neural Networks for Automatic Speech Recognition D Chen – 2015 – cse.ust.hk … regularization methods, such as the dropout method [13] • different deep learning architectures such as the deep convolutional neural net- work [14] and the deep recurrent neural network [15]; … dialog systems which is particularly successful when the amount of labeled training … Related articles

Human Affect Recognition: Audio-Based Methods B Schuller, F Weninger – Wiley Encyclopedia of Electrical and …, 2015 – Wiley Online Library … long-range context, possibly spanning across multiple utterances, can be exploited by means of (deep) recurrent neural networks (RNNs), which … disorders, from healthy adults to adults with voice pathologies, from interactions with spoken language dialog systems to emotional … Cited by 1 Related articles

Towards Universal Paraphrastic Sentence Embeddings JWMBK Gimpel, K Livescu – arXiv preprint arXiv: …, 2015 – pdfs.semanticscholar.org … We present the interesting result that simple com- positional architectures based on updated vector averaging vastly outperform long short-term memory (LSTM) recurrent neural networks and that these simpler ar- chitectures allow us to learn models with superior generalization … Related articles All 4 versions

A New SVM Kernel for Keyword Spotting Using Confidence Measures Y Ben Ayed – International Journal on Artificial Intelligence Tools, 2015 – World Scientific … automatic speech recognition applications include dialogue systems, speech based interfaces and voice controlled systems which can be found in automatic applica- tions for data collection. 3,4 Due to the amount of potential … Related articles

Synthesis of Speech & Speaker Recognition Using Data Driven Approach L Kumari, R Guha, G Gera – Citeseer … 4) Spoken Dialog System: Having decoded the speech signal into a sequence of words, or a hypothesized sequence of words … MJ Witbrock, and K.-F. Lee, “Speaker- Independent Recognition of Connected Utterances Using Recurrent and Non recurrent Neural Networks,” Proc. … Related articles All 2 versions

Learning for Spoken Dialog Systems with Discriminative Graphical Models Y Ma – 2015 – etd.ohiolink.edu Page 1. LEARNING FOR SPOKEN DIALOG SYSTEMS WITH DISCRIMINATIVE GRAPHICAL MODELS DISSERTATION … Page 2. c Copyright by Yi Ma 2015 Page 3. ABSTRACT A statistical spoken dialog system must keep track of what the user wants at any point during a dialog. … Related articles All 3 versions

Reference-free and Confidence-independent Binary Quality Estimation for Automatic Speech Recognition H Zamani, JGC de Souza, M Negri, M Turchi… – CLiC it, 2015 – iris.unito.it … speech recognition systems used to transcribe audio recordings from differ- ent sources (eg YouTube videos, TV programs, corporate meetings), or the dialogue systems for human … ASR Error Detection Using Recurrent Neural Network Language Model and Complementary ASR … Related articles All 3 versions

Adequacy–fluency metrics: Evaluating MT in the continuous space model framework RE Banchs, LF D’Haro, H Li – IEEE/ACM Transactions on Audio, …, 2015 – ieeexplore.ieee.org Page 1. 2329-9290 (c) 2015 IEEE. Personal use is permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org/ publications_standards/publications/rights/index.html for more information. This … Cited by 6 Related articles All 3 versions

Natural language understanding for partial queries X Liu, A Celikyilmaz, R Sarikaya – 2015 IEEE Workshop on …, 2015 – ieeexplore.ieee.org … for Robust Domain Classification And Track- ing in Dialogue Systems”, Proceedings of Interspeech, Singapore, 2014. [6] P. Xu and R. Sarikaya, “Contextual domain classification in spoken language understanding systems using recurrent neural network”, Proceedings of … Cited by 1 Related articles All 3 versions

Box: Natural Language Processing Research Using Amazon Web Services A Axelrod – The Prague Bulletin of Mathematical Linguistics, 2015 – degruyter.com … There are many open- source tools that each perform one NLP-related task well, such as speech recognition, optical character recognition, text-to-speech, dialog systems, and so on. … Recurrent Neural Network based Language Model. INTERSPEECH, 2010. … Cited by 1 Related articles All 5 versions

Evolution of reflexive signals using a realistic vocal tract model AS Warlaumont, AM Olney – Adaptive Behavior, 2015 – adb.sagepub.com … They evolved the connection weights of a simple recurrent neural network using a genetic algorithm where fitness depended on the ability to accurately detect which calls came from members of the same species and which belonged to other species. … Related articles All 3 versions

A probabilistic framework for representing dialog systems and entropy-based dialog management through dynamic stochastic state evolution J Wu, M Li, CH Lee – IEEE/ACM Transactions on Audio, Speech …, 2015 – ieeexplore.ieee.org … Abstract In this paper, we present a probabilistic framework for goal-driven spoken dialog systems. A new dynamic stochastic state … I. INTRODUCTION SPOKEN dialog systems enable a human user to acquire information and services by interacting with a computer agent … Cited by 4 Related articles All 6 versions

Soft-Computational Techniques and Spectro-Temporal Features for Telephonic Speech Recognition: An Overview and Review of Current State of the Art M Sharma, KK Sarma – Handbook of Research on Advanced …, 2015 – books.google.com … Recurrent Neural Networks (RNN) can be used through sophisticated dynamics which can be used for data analysis techniques, pattern recognition, signal … to 95% which makes the proposed solution a feasible one with addition to the existing spoken dialogue systems such as … Related articles All 3 versions

Bilingual continuous-space language model growing for statistical machine translation R Wang, H Zhao, BL Lu, M Utiyama… – IEEE/ACM Transactions …, 2015 – ieeexplore.ieee.org Page 1. 2329-9290 (c) 2015 IEEE. Personal use is permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org/ publications_standards/publications/rights/index.html for more information. This … Cited by 9 Related articles All 7 versions

Text Based Emotion Recognition: A Survey CR Chopade – ijsr.net … system should be applied in different kinds of the Human computer interaction systems, such as dialogue systems, automatic answering … [7] Frinken, V.; Fischer, A.; Manmatha, R.; Bunke, H. “A Novel Word Spotting Method Based on Recurrent Neural Networks” Pattern Analysis … Cited by 1 Related articles

A system for recognizing human emotions based on speech analysis and facial feature extraction: applications to Human-Robot Interaction M Rabiei – 2015 – dspace-uniud.cineca.it … 1]. Speech emotion recognition has also been used in call center applications and mobile communication [2]. Some works tried to incorporate spoken dialogue system technology and service robots. Psychologists believe that … Related articles All 2 versions

Speaker de-identification using diphone recognition and speech synthesis T Justin, V Štruc, S Dobrišek, B Vesnicer… – Automatic Face and …, 2015 – ieeexplore.ieee.org … The word- recognition system using this database was developed and presented in [11]. The goal here (ie, [11]) is to build an automatic speech dialogue system for querying flight information, thus, the vocabulary in the database is related to this task. … Cited by 1 Related articles All 9 versions

MERL Annual Report 2015 RC Waters – 2015 – pdfs.semanticscholar.org Page 1. MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com MERL Annual Report 2015 Waters, RC TR2015-000 June 2015 Abstract Welcome to Mitsubishi Electric Research Laboratories (MERL), the …

How to evaluate ASR errors impact on NER? MAB Jannet, O Galibert, M Adda-Decker, S Rosset – errare2015.racai.ro … cations such as speech-to-speech translation, spoken informa- tion retrieval or spoken language dialog systems. … 712–721. [3] AL Maas, QV Le, TM O’Neil, O. Vinyals, P. Nguyen, and AY Ng, “Recurrent neural networks for noise reduction in robust asr.” in INTERSPEECH, 2012. … Cited by 1 Related articles

Culture Clubs Processing Speech by Deriving and Exploiting Linguistic Subcultures DG Brizan – 2015 – gc.cuny.edu … as a tool for actors, it is become valuable to customer service representatives and people in other fields. A dialogue system could be created from the speech signals of one linguistic subculture and could generate prompts to the human interlocutor using text-to-speech (TTS). … Related articles All 2 versions

Cross-Lingual Cross-Media Content Linking: Annotations and Joint Representations (Dagstuhl Seminar 15201) AG Hauptmann, J Hodson, J Li, N Sebe… – Dagstuhl …, 2015 – drops.dagstuhl.de … This is the case for most of web data, emails, dialogue systems, and social media chatter. … Results are overwhelming when used to jointly model images or videos along with captions using deep learning approaches like Recurrent neural networks and multimodal log-bilinear … Related articles All 3 versions

Decoupling word-pair distance and co-occurrence information for effective long history context language modeling TY Chong, RE Banchs, ES Chng… – IEEE/ACM Transactions …, 2015 – ieeexplore.ieee.org Page 1. 2329-9290 (c) 2015 IEEE. Personal use is permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org/ publications_standards/publications/rights/index.html for more information. This … Cited by 1 Related articles All 3 versions

Spectral learning with non negative probabilities for finite state automaton H Glaude, C Enderli, O Pietquin – 2015 IEEE Workshop on …, 2015 – ieeexplore.ieee.org Page 1. SPECTRAL LEARNING WITH NON NEGATIVE PROBABILITIES FOR FINITE STATE AUTOMATON Hadrien Glaude *† Cyrille Enderli † Olivier Pietquin * †Thales Airborne Systems, Elancourt, France *Univ. Lille, CRIStAL … Cited by 1 Related articles

Spectral Learning With Proper Probabilities For Finite State Automaton H Glaude, C Enderli, O Pietquin – Automatic Speech Recognition and …, 2015 – hal.inria.fr Page 1. SPECTRAL LEARNING WITH PROPER PROBABILITIES FOR FINITE STATE AUTOMATON Hadrien Glaude, Cyrille Enderli, Olivier Pietquin To cite this version: Hadrien Glaude, Cyrille Enderli, Olivier Pietquin. SPECTRAL LEARNING WITH … Cited by 1 Related articles All 4 versions

Integrating natural language processing with image document analysis: what we learned from two real-world applications J Chen, H Cao, P Natarajan – International Journal on Document Analysis …, 2015 – Springer Related articles All 3 versions

Compensating changes in speaker position for improved voice-based human-robot communication R Gomez, K Nakamura, T Mizumoto… – … ), 2015 IEEE-RAS 15th …, 2015 – ieeexplore.ieee.org … Ramachandran, D. and Gupta, R.,”Landmark- based Location Belief Tracking in a Spoken Dialog System”, In Proceedings … Recognition of Reverberated Speech using Multi-Channel Correlation Shaping Dereverberation and BLSTM Recurrent Neural Networks”, In Proceedings … Related articles

Semi-Autonomous Data Enrichment and Optimisation for Intelligent Speech Analysis Z Zhang – 2015 – mediatum.ub.tum.de … Moreover, for distant-talk ASR, Long Short-Term Memory (LSTM) recurrent neural networks, which are known to be well-suited to context-sensitive … Generally, a Wizard-of-Oz (WOZ) is designed at the beginning (eg, a multi-model dialogue system), then elicits the speakers to a … Cited by 1 Related articles All 3 versions

Nastalique segmentation-based approach for Urdu OCR S Hussain, S Ali – International Journal on Document Analysis and …, 2015 – Springer … the feature set. The character transcription is used for training and recognition. The Recurrent Neural Networks (RNNs) are used for the contextual processing of the characters of text line images. Testing on synthesized data … Related articles All 3 versions

Speech recognition based confidence measures for building voices from untranscribed speech TS Godambe – 2015 – web2py.iiit.ac.in Page 1. Speech recognition based confidence measures for building voices from untranscribed speech Thesis submitted in partial fulfillment of the requirements for the degree of MS by Research in Electronics and Communication Engineering by … Related articles

State of the art in hand and finger modeling and animation N Wheatland, Y Wang, H Song, M Neff… – Computer Graphics …, 2015 – Wiley Online Library Our site uses cookies to improve your experience. You can find out more about our use of cookies in About Cookies, including instructions on how to turn off cookies if you wish to do so. By continuing to browse this site you agree … Cited by 7 Related articles All 9 versions

[BOOK] Advances in Artificial Intelligence and Soft Computing G Sidorov, SN Galicia-Haro – 2015 – Springer … Prize from Springer: € 400; prize from SMIA: € 400 Second place: “Dynamic Systems Identification and Control by Means of Complex-Valued Recurrent Neural Networks,” by Ieroham Baruch, Victor Arellano Quintana, and Edmundo Pérez Reynaud (Mexico) … Related articles All 2 versions

Deep learning approaches to problems in speech recognition, computational chemistry, and natural language text processing GE Dahl – 2015 – tspace.library.utoronto.ca Page 1. Deep learning approaches to problems in speech recognition, computational chemistry, and natural language text processing by George Edward Dahl A thesis submitted in conformity with the requirements for the degree … Cited by 3 Related articles All 5 versions

Resource-Dependent Acoustic and Language Modeling for Spoken Keyword Search IF Chen – 2015 – smartech.gatech.edu Page 1. RESOURCE-DEPENDENT ACOUSTIC AND LANGUAGE MODELING FOR SPOKEN KEYWORD SEARCH A Dissertation Presented to The Academic Faculty By I-Fan Chen In Partial Fulfillment Of the Requirements … Related articles

Effective use of cross-domain parsing in automatic speech recognition and error detection MA Marin – 2015 – digital.lib.washington.edu … information, we attempt to detect their location and extent (within the ASR hypothesis), as well as the type, in order to handle them effectively during the subsequent clarification request made by the dialog system component. In particular we are interested in two types … Cited by 2 Related articles All 2 versions

[BOOK] Advances in Artificial Intelligence and Its Applications: 14th Mexican International Conference on Artificial Intelligence, MICAI 2015, Cuernavaca, Morelos, … OP Lagunas, OH Alcántara, GA Figueroa – 2015 – books.google.com … 2014 Presidential Election,” by Jhon Adrián Cerón-Guzmán and Elizabeth León (Colombia) Prize from Springer: € 400; prize from SMIA: € 400 Second “Dynamic Systems Identification and Control by Means of Complex-Valued place: Recurrent Neural Networks,” by Ieroham … Related articles

Code-switching event detection by using a latent language space model and the delta-Bayesian information criterion CH Wu, HP Shen, CS Hsu – IEEE/ACM Transactions on Audio, …, 2015 – ieeexplore.ieee.org … switching event detection is becoming indispensable in human–machine communication applications, particularly in multilingual spoken dialog systems. … The detectors can be implemented using recurrent neural networks (RNNs), GMMs, HMMs, and SVMs at both frame and … Cited by 1 Related articles All 5 versions

Spoken content retrieval—beyond cascading speech recognition with text retrieval L Lee, J Glass, H Lee, C Chan – IEEE/ACM Transactions on …, 2015 – ieeexplore.ieee.org Page 1. IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 23, NO. 9, SEPTEMBER 2015 1389 Spoken Content Retrieval—Beyond Cascading Speech Recognition with Text Retrieval … Cited by 6 Related articles All 6 versions