Using Automatically Transcribed Dialogs to Learn User Models in a Spoken Dialog System

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.

• Type: Conference
• Year: 2008
• Where: ACL
• Authors: Umar Syed, Jason Williams
• Keywords: ACL 2008, Computational Linguistics, Manual Transcription Effort, Spoken Dialog System, User Models