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End-of-utterance detection

The current dialogue system used at the Dialogue Lab, GoDiS, depends on cut-off values to control turn-taking. This means that when the user has not spoken for a period of time, the system assumes the user is finished and takes the turn. This can lead to both interruptions and unnecessary long waits for the user.

To solve this problem, the system has to be able to detect when a speaker is finished or when he is just making a pause within his utterance. If the system can reliably detect the users end-of-utterance, it can take the turn more rapidly when the user is finished (and avoid interrupting the user when he/she is not finished).

To detect end of utterance, we assume that the system needs information from several sources: syntactic information, prosodic information and also information state. We will create a statistical language model for end-of-utterance detection, using machine learning. For this we will use the Weka toolkit.

We will attempt to create a model that allows the system to differentiate between user pauses within an utterance, and user pauses at the end of an utterance.

Funding: CLT internal.

Duration: August 2011 - October 2012

Researchers: Kristina Lundholm Fors, Staffan Larsson (supervisor).

 

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Page updated: 2014-09-09 15:34

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