Deep Learning & Dialog Systems 2014


Deep Learning

Notes:

Janus Recognition Toolkit (JRTk) has been used by the Interactive System Lab in many projects for speech recognition, such as BABEL.  The Babel Program (IARPA) is developing agile and robust speech recognition technology that can be rapidly applied to any human language in order to provide effective search capability for analysts to efficiently process massive amounts of real-world recorded speech.

See also:

100 Best Deep Learning Videos | 100 Best GitHub: Deep LearningSkipgram & Deep Learning 2014


A historical perspective of speech recognition X Huang, J Baker, R Reddy – Communications of the ACM, 2014 – dl.acm.org … However, many algorithmic improvements have been made, such as how to use distributed algorithms for the deep learning task. … 42. Williams, J. and Young, S. Partially observable Markov decision processes for spoken dialog systems. … Cited by 12 Related articles

Word-based dialog state tracking with recurrent neural networks M Henderson, B Thomson… – 15th Annual Meeting of …, 2014 – anthology.aclweb.org … The challenge is based on a large corpus collected using a variety of telephone- based dialog systems in the domain of finding a restaurant in … underlying representations of the input, has been found effective as an initialisation technique in deep learning (Vincent et al., 2008). … Cited by 4 Related articles All 9 versions

Feature enhancement by deep LSTM networks for ASR in reverberant multisource environments F Weninger, J Geiger, M Wöllmer, B Schuller… – Computer Speech & …, 2014 – Elsevier … feature-space de-reverberation. There, we also consider deep learning with pre-training, where the first layers are trained to de-noising and subsequent layer(s) are trained to perform de-reverberation. While the CHiME WSJ … Cited by 9 Related articles All 4 versions

Application of deep belief networks for natural language understanding R Sarikaya, GE Hinton, A Deoras – IEEE/ACM Transactions on Audio, …, 2014 – dl.acm.org … 1481–1488. [13] D. Erhan, Y. Bengio, A. Courville, P. Manzagol, and P. Vincent, “Why Does Unsupervised Pre-training Help Deep Learning?,” J. Mach. … Ruhi Sarikaya is a principal scientist and the manager of language understanding and dialog systems group at Microsoft. … Cited by 5 Related articles All 7 versions

Deep learning of knowledge graph embeddings for semantic parsing of twitter dialogs L Heck, H Huang – IEEE Global Conference on Signal and …, 2014 – msr-waypoint.net … Index Terms—deep learning, semantic parsing, Twitter, dialog … Rep. MSR-TR-2014-70, 2014. [16] L. Wang, L. Heck, and D. Hakkani-Tür, “Leveraging semantic web search and browse sessions for multi-turn spoken dialog systems,” in Proceedings of the International … Cited by 1 Related articles All 7 versions

Knowledge Acquisition Strategies for Goal-Oriented Dialog Systems A Pappu, AI Rudnicky – 15th Annual Meeting of the Special …, 2014 – anthology.aclweb.org … system, education models, human learning System Predicted Researcher-Interests 4 florian metze dialogue systems, speech recogni- tion … dy- namics System Predicted Researcher-Interests 6 alexander hauptmann discriminatively trained models, deep learning, computer vision … Cited by 2 Related articles All 7 versions

Deriving local relational surface forms from dependency-based entity embeddings for unsupervised spoken language understanding YN Chen, D Hakkani-Tür, G Tur – Proceedings of SLT, 2014 – researchgate.net … surface forms are learned from dependency-based entity embeddings, which encode the contexts of entities from dependency trees in a deep learning model. … Traditional spo- ken dialogue systems (SDS) are trained with annotated examples and support limited domains. … Cited by 2

Dynamically supporting unexplored domains in conversational interactions by enriching semantics with neural word embeddings YN Chen, AI Rudnicky – Proceedings of SLT, 2014 – researchgate.net … Spoken dialogue systems (SDS) are appearing on smart-phones and allow users to launch applications via spontaneous speech. … With the recent advance of deep learning techniques, the continuous-valued word embeddings have further boosted the state-of-the-art results in … Cited by 2

Learning Situated Knowledge Bases through Dialog A Pappu, AI Rudnicky – Fifteenth Annual Conference of …, 2014 – mazsola.iit.uni-miskolc.hu … The study took place in the context of EventSpeak Dialog System that informs people about upcoming talks/events of their interest and ongo- ing work of other researchers on a university campus. … search, learning to rank 13. crowdsourcing, deep learning 14. … Cited by 3 Related articles All 5 versions

Eye gaze for spoken language understanding in multi-modal conversational interactions D Hakkani-Tür, M Slaney, A Celikyilmaz… – Proceedings of the 16th …, 2014 – dl.acm.org … [5] L. Deng, J. Li, J.-T. Huang, K. Yao, D. Yu, F. Seide, M. Seltzer, G. Zweig, X. He, and J. Williams. Recent advances in deep learning for speech research at microsoft. … [9] T. Misu, A. Raux, I. Lane, J. Devassy, and R. Gupta. Situated multi-modal dialog system in vehicles. … Cited by 3 Related articles All 4 versions

Improving deep neural network acoustic modeling for audio corpus indexing under the IARPA Babel program X Cui, B Kingsbury, J Cui… – submitted to …, 2014 – mazsola.iit.uni-miskolc.hu … M. Heldner, “An instantaneous vec- tor representation of delta pitch for speaker-change prediction in conversational dialogue systems,” ICASSP 2008 … GE Hinton, “Vocal tract length perturbation (VTLP) improves speech recognition, in ICML Workshop on Deep Learning for Audio … Cited by 1 Related articles All 5 versions

A generalized rule based tracker for dialogue state tracking K Sun, L Chen, S Zhu, K Yu – submitted to IEEE SLT 2014, 2014 – aiexp.info … [1] Jason D Williams, “Challenges and opportunities for state tracking in statistical spoken dialog systems: Re- sults … Huang, Kaisheng Yao, Dong Yu, Frank Seide, Michael Seltzer, Geoff Zweig, Xi- aodong He, Jason Williams, et al., “Recent advances in deep learning for speech … Cited by 1 Related articles

Deep Neural Networks For Spoken Dialog Systems C MAIN – 2014 – macs.hw.ac.uk … 21 2.3 Deep Learning for State Tracking in Spoken Dialogue Systems . . . . . 23 … 2.3 Deep Learning for State Tracking in Spoken Dialogue Systems State tracking in SDSs is a complex task with issues that it needs to deal with such as noisy conditions and ambiguity. … Related articles

Leveraging Frame Semantics and Distributional Semantics for Unsupervised Semantic Slot Induction in Spoken Dialogue Systems YN Chen, WY Wang, AI Rudnicky – cs.cmu.edu … success that implements this distributional theory is Latent Semantic Analy- sis [3]. Recently, with the advance of deep learning techniques, the … frame-semantic parsing to automatically induce and adapt the se- mantic ontology for designing spoken dialogue systems (SDS) in an … Cited by 4 Related articles All 3 versions

Evaluating a Spoken Dialogue System that Detects and Adapts to User Affective States D Litman, K Forbes-Riley – 15th Annual Meeting of the Special Interest …, 2014 – aclweb.org … is and isn’t effective at promoting deep learning. In Intelligent Tutoring Systems Conference, pages 245– 254, Pittsburgh, PA, USA. K. Forbes-Riley and D. Litman. 2009. A user modeling-based performance analysis of a wizarded uncertainty-adaptive dialogue system corpus. … Related articles All 6 versions

Deep Generative and Discriminative Models for Speech Recognition L Deng – wissap.iiit.ac.in … Page 25. Li Deng, Dong Yu (Microsoft Research) Geoffrey Hinton (University of Toronto) Deep Learning for Speech Recognition and Related Applications … Page 53. Outline • Advances in deep learning for features/ representations • Advances in deep learning for models/ … Related articles

FIONA: a platform for embodied cognitive agents C Alvarez, L Fernández Cossío – Proceedings of the second …, 2014 – dl.acm.org … Author Keywords FIONA, artificial intelligence, virtual robots, virtual agents, artificial vision, cognitive architectures, dialog systems, speech recognition system, deep learning, neural networks ACM Classification Keywords D.1.7 Programming Techniques: Visual Programming …

Optimizing Generative Dialog State Tracker via Cascading Gradient Descent BJ Lee, W Lim, D Kim, KE Kim – 15th Annual Meeting of the …, 2014 – anthology.aclweb.org … 1 Introduction Spoken dialog systems, a field rapidly growing with the spread of smart mobile devices, has to deal with challenges to … The model used in this paper can naturally devel- oped by adding hidden layers, and ultimately deep learning techniques could be applicable. … All 8 versions

AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring BD Nye, AC Graesser, X Hu – … Journal of Artificial Intelligence in Education, 2014 – Springer … Using a domain-independent reactive planner to implement a medical dialogue system. In T. Bickmore (Ed.), AAAI fall symposium on systems for health communication (pp. 24–31). … AutoTutor improves deep learning of computer literacy: Is it the dialog or the talking head? … Related articles

Joint Semantic Utterance Classification and Slot Filling with Recursive Neural Networks DZ Guo, G Tur, W Yih, G Zweig – research.microsoft.com … Performing intent determination and slot filling together in one model simplifies a dialog system, since only one model needs to be … and A Ng, “Grounded com- positional semantics for finding and describing images with sentences,” in NIPS Deep Learning Workshop, 2013. … Related articles All 6 versions

Personal Knowledge Graph Population From User Utterances In Conversational Understanding X Li, G Tur, DHTQ Li – msr-waypoint.com … Intent: Get Price Slots: good: gas; cost relative: cheapest; location: (lat,long) Typically, spoken dialog queries to a dialog system may be … various classification methods such as Boosting [11, 12, 13], support vector machines (SVMs) [14], and more recently deep learning [15, 16]. …

Noisy speech recognition using blind spatial subtraction array technique and deep bottleneck features N Kitaoka, T Hayashi, K Takeda – Asia-Pacific Signal and …, 2014 – ieeexplore.ieee.org … the field of pattern recognition research, and Deep Neural Networks (DNN) which have been trained using deep learning have achieved … Saruwatari, K. Shikano, “Real-time implementation of blind spatial subtraction array for hands-free robot spoken dialogue system,” IEEE/RSJ …

From here to AGI: A roadmap to the realization of human-level artificial general intelligence B Goertzel, G Yu – Neural Networks (IJCNN), 2014 International …, 2014 – ieeexplore.ieee.org … world Animated Agent Carrying out of simple “scientific” experi- mentation in game world Mobile Robot • Building structures from blocks and other simple objects • Supplementation of third-party speech-to-text with deep learning based speech-to-text Dialogue System • Ability to … Related articles

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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 … spite them being crucial for real-life applications such as retrieval, dialogue systems and computer-mediated human- to-human conversation … For real- ising these ambitious goals, deep learning (Hinton et al., 2012) combined with neuroevolutionary methods and non- parametric … Cited by 1 Related articles All 2 versions

Acoustic-Prosodic Entrainment and Rapport in Collaborative Learning Dialogues N Lubold, H Pon-Barry – Proceedings of the 2014 ACM workshop on …, 2014 – dl.acm.org … can serve as a guide for teachers when students are engaged in collaborative activity. In tutorial dialogue systems, de- tecting rapport has implications for improving dialogue suc- cess and quality. … Strong community, deep learning: Exploring the link. … Related articles All 3 versions

Adaptive Tutoring for Self-Regulated Learning: A Tutorial on Tutoring Systems RA Sottilare, AM Sinatra – researchgate.net … training. The ultimate goal for ITSs is to impact learning with effect sizes equivalent to raising average (“C”) students to experts (“A” students) through tailored instruction and reinforcement of deep learning principles. In other …

Language Learning via Unsupervised Corpus Analysis B Goertzel, C Pennachin, N Geisweiller – Engineering General Intelligence …, 2014 – Springer … We consider the approach described here as “deep learning” oriented because it is based on hierarchical pattern recognition in linguistic data: identifying patterns, then patterns among these patterns, etc., in a hierarchy that allows “higher level” (more abstract) patterns to feed …

Serious Games in Education Towards the standardization of the teaching-learning process N HAMDAOUI, MK IDRISSI… – ADVANCES in …, 2014 – researchgate.net … to which the learner must be able to customize his/her learning experience to his/her own learning style and be able to try new styles at the same time; the ‘identity principle’which stands for the fact that deep learning calls for an … User Models in Dialog Systems, 1989: p. 35-51. … Related articles All 3 versions

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