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Hidden Information State System A Statistical Spoken Dialogue System M. Gašić, F. Jurčíček, S. Keizer, F. Mairesse, B. Thomson, K. Yu and S. Young Cambridge.

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Presentation on theme: "Hidden Information State System A Statistical Spoken Dialogue System M. Gašić, F. Jurčíček, S. Keizer, F. Mairesse, B. Thomson, K. Yu and S. Young Cambridge."— Presentation transcript:

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2 Hidden Information State System A Statistical Spoken Dialogue System M. Gašić, F. Jurčíček, S. Keizer, F. Mairesse, B. Thomson, K. Yu and S. Young Cambridge University Engineering Department | {mg436, fj228, sk561, farm2, brmt2, ky219, SEMANTIC DECODER AUTOMATIC SPEECH RECOGNISER NATURAL LANGUAGE GENERATOR TEXT TO SPEECH SYNTHESISER Machine sentence Machine dialogue action N-best list of user utterances SPOKEN DIALOGUE SYSTEM N-best list of user dialog. actions N-best list of user dialog. actions ãuNãuN ãu1ãu1 ãu2ãu2 gKgK g1g1 g2g2 Observation N-best list of user dialogue actions User goal partitions built according to the ontology rules Dialogue history Grounding states Hypotheses Every possible combination of observation, user goal and dialogue history Belief state Distribution over hypotheses h M =(ã u 2,p 1,g K ) h 1 =(ã u 1,p 2,g 3 ) h 2 =(ã u 3,p 2,g 2 ) p1p1 p2p2 h4h4 h1h1 h2h2 h3h3 h5h5 PARTIALLY OBSERVABLE MARKOV DECISION PROCESS-BASED DIALOGUE MANAGER Belief state is mapped to a point in the summary space Summary Space āmām The region the point falls in has a summary action associated to it by the policy Machine summary action amam Additional information from belief state is added to summary action Machine dialogue action DIALOGUE STATE MAINTAINING ACTION SELECTION DIALOGUE MANAGER POLICY OPTIMISATION[1] Reinforcement learning with a simulated user Monte Carlo Control algorithm for grid-based learning For tourist information domain: - 100,000 dialogues needed to train the optimal policy - The optimal policy divided the summary space in 1500 regions POLICY ATK – LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION Acoustic model – Speaker independent HMM, trained on 39 hours of data Language model - Statistical trigram, trained on 80M words Output – Confidence scored N-best list SEMANTIC TUPLE CLASSIFIERS[2] utterance(u) dialogue act type (slot 1 =value 1, … ) Probabilistic SVM classifiers predicting: P(slot i =value i |u), P(dia_act_type|u) Trained on 8000 sentences HMM-BASED SYNTHESISER[3] wordphonemeacoustics Flite Globally-tied density HMM Trained on 1 hour of CMU ARCTIC data STATISTICAL GENERATION Currently handcrafted Future work – data driven approach to optimise naturalness and style based on context Acknowledgements This research was funded by the UK EPSRC under grant agreement EP/F013930/1 and by the EU FP7 Programme under grant agreement (CLASSIC project: References [1] M Gašić, S Keizer, F Mairesse, J Schatzmann, B Thomson, K Yu, and SJ Young. Training and Evaluation of the HIS POMDP Dialogue System in Noise. In SigDial, Columbus, Ohio, [2] F Mairesse, M Gašić, F Jurčíček, S Keizer, B Thomson, K Yu, and SJ Young. Spoken Language Understanding from Unaligned Data using Discriminative Classification Models. Submitted to ICASSP09. [3] K Yu, T Toda, M Gašić, S Keizer, F Mairesse, B Thomson, and SJ Young. Probabilistic Modelling of F0 in Unvoiced Regions in HMM Based Speech Synthesis. Submitted to ICASSP09. Update history


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