Neural Dialogue Context Online End-of-Turn Detection
By Ryo Masumura, Tomohiro Tanaka, Atsushi Ando, Ryo Ishii, Ryuichiro Higashinaka, Yushi Aono. 2018
34 citations recorded by the source corpus.
This paper proposes a fully neural network based dialogue-context online end-of-turn detection method that can utilize long-range interactive information extracted from both speaker’s utterances and collocutor’s utterances. The proposed method combines multiple time-asynchronous long short-term memory recurrent neural networks, which can capture speaker’s and collocutor’s multiple sequential features, and their interactions. On the assumption of applying the proposed method to spoken dialogue systems, we introduce speaker’s acoustic sequential features and collocutor’s linguistic sequential features, each of which can be extracted in an online manner. Our evaluation confirms the effectiveness of taking dialogue context formed by the speaker’s utterances and collocutor’s utterances into consideration.
Subfields: Artificial Intelligence
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Citations in the corpus
- Generative Spoken Dialogue Language Modeling (2022, neutral): “More recently, more sophisticated machine learning-based models of turn-taking have been introduced (Meena et al., 2014; Skantze, 2017; Roddy et al., 2018; Masumura et al., 2018).”
- TurnGPT: a Transformer-based Language Model for Predicting Turn-taking in Spoken Dialog (2020, methodology): “However, we argue that previous models that have combined linguistic and prosodic cues (cf. Meena et al. 2014; Skantze 2017; Roddy et al. 2018; Masumura et al. 2019) have used too simplistic models of linguistic turn-constructional units.”