A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Training a real-world POMDP-based dialogue system
NAACL-HLT-Dialog '07 Proceedings of the Workshop on Bridging the Gap: Academic and Industrial Research in Dialog Technologies
ACM Transactions on Speech and Language Processing (TSLP)
Reinforcement learning for parameter estimation in statistical spoken dialogue systems
Computer Speech and Language
An unsupervised approach to user simulation: toward self-improving dialog systems
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Exploiting machine-transcribed dialog corpus to improve multiple dialog states tracking methods
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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We use an EM algorithm to learn user models in a spoken dialog system. Our method requires automatically transcribed (with ASR) dialog corpora, plus a model of transcription errors, but does not otherwise need any manual transcription effort. We tested our method on a voice-controlled telephone directory application, and show that our learned models better replicate the true distribution of user actions than those trained by simpler methods and are very similar to user models estimated from manually transcribed dialogs.