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Spoken Dialogue Management Using Probabilistic Reasoning (2000)
| Content Provider | CiteSeerX |
|---|---|
| Author | Roy, Nicholas Thrun, Sebastian Pineau, Joelle |
| Abstract | Spoken dialogue managers have benefited from using stochastic planners such as Markov Decision Processes (MDPs). However, so far, MDPs do not handle well noisy and ambiguous speech utterances. We use a Partially Observable Markov Decision Process (POMDP)-style approach to generate dialogue strategies by inverting the notion of dialogue state; the state represents the user's intentions, rather than the system state. We demonstrate that under the same noisy conditions, a POMDP dialogue manager makes fewer mistakes than an MDP dialogue manager. Furthermore, as the quality of speech recognition degrades, the POMDP dialogue manager automatically adjusts the policy. 1 Introduction The development of automatic speech recognition has made possible more natural human-computer interaction. Speech recognition and speech understanding, however, are not yet at the point where a computer can reliably extract the intended meaning from every human utterance. Human speech can be both n... |
| File Format | |
| Publisher Date | 2000-01-01 |
| Access Restriction | Open |
| Subject Keyword | Speech Recognition Human Speech Partially Observable Markov Decision Process Automatic Speech Recognition Natural Human-computer Interaction Spoken Dialogue Manager Stochastic Planner Speech Understanding System State Intended Meaning Mdp Dialogue Manager Style Approach Noisy Condition Pomdp Dialogue Manager Speech Recognition Degrades Dialogue Strategy Ambiguous Speech Utterance Markov Decision Process Dialogue State Human Utterance |
| Content Type | Text |
