A Finite-State Turn-Taking Model for Spoken Dialog Systems
By Antoine Raux, Maxine Eskénazi. 2009
140 citations recorded by the source corpus.
This paper introduces the Finite-State Turn-Taking Machine (FSTTM), a new model to control the turn-taking behavior of conversational agents. Based on a non-deterministic finite-state machine, the FSTTM uses a cost matrix and decision theoretic principles to select a turn-taking action at any time. We show how the model can be applied to the problem of end-of-turn detection. Evaluation results on a deployed spoken dialog system show that the FSTTM provides significantly higher responsiveness than previous approaches.
Subfields: Artificial Intelligence, Language and Linguistics
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In Artificial Intelligence
- Wei-Ning Hsu
- Yossi Adi
- Jade Copet
- Emmanuel Dupoux
- Tu Nguyen
- Evgeny Kharitonov
- Abdel-rahman Mohamed
- Gabriel Synnaeve
In Language and Linguistics
- Alejandro Vega
- Timo Baumann
- Jens Edlund
- Emmanuel Dupoux
- Mattias Heldner
- Wei-Ning Hsu
- Nigel G. Ward
- Francisco Torreira
Citations in the corpus
- Generative Spoken Dialogue Language Modeling (2022, methodology): “Simple turn-taking models using finite-state machines have been proposed to predict the distribution and durations of turn-taking events (Cassell et al., 2001; Thórisson, 2002; Raux and Eskenazi, 2009).”
- Pauses, gaps and overlaps in conversations (2010, background): “…state label; states describe the joint vocal activity of both speakers, building on a tradition of computational models of interaction (e.g. Brady, 1968; Dabbs & Ruback, 1984, 1987; Jaffe & Feldstein, 1970; Laskowski & Shriberg, 2009; Norwine & Murphy, 1938; Raux & Eskenazi, 2009; Sellen, 1995).”
- Investigating Speech Features for Continuous Turn-Taking Prediction Using LSTMs (2018, unknown): “[7, 8, 9]) that wait until a speaker has stopped speaking for a given length of time to form a binary prediction whether the speaker will continue speaking (HOLD) or not (SHIFT).”
- Prosodic and temporal features for language modeling for dialog (2012, background): “For evaluation purposes we also ignored sentence-end tags; this is because the utility of temporal and prosodic information for endpoint prediction is already well-known (Ferrer et al., 2003; Raux and Eskenazi, 2009), and we wanted to isolate the benefit for word prediction.”