Neural Dialogue Context Online End-of-Turn Detection
Ryo Masumura, Tomohiro Tanaka, Atsushi Ando, Ryo Ishii, Ryuichiro Higashinaka, Yushi Aono · 2018
This paper proposes a fully neural network based dialogue-context online end-of-turn detection method that can utilize longrange interactive information extracted from both target speaker's and interlocutor's utterances.In the proposed method, we combine multiple time-asynchronous long short-term memory recurrent neural networks, which can capture target speaker's and interlocutor's multiple sequential features, and their interactions.On the assumption of applying the proposed method to spoken dialogue systems, we introduce target speaker's acoustic sequential features and interlocutor'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 target speaker's utterances and interlocutor's utterances into consideration.