no code implementations • IWSLT (EMNLP) 2018 • Kaho Osamura, Takatomo Kano, Sakriani Sakti, Katsuhito Sudoh, Satoshi Nakamura
In this paper, a neural sequence-to-sequence ASR is used as feature processing that is trained to produce word posterior features given spoken utterances.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 22 Dec 2023 • Atsunori Ogawa, Naohiro Tawara, Takatomo Kano, Marc Delcroix
Confidence estimation, in which we estimate the reliability of each recognized token (e. g., word, sub-word, and character) in automatic speech recognition (ASR) hypotheses and detect incorrectly recognized tokens, is an important function for developing ASR applications.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 7 Jun 2023 • Kohei Matsuura, Takanori Ashihara, Takafumi Moriya, Tomohiro Tanaka, Takatomo Kano, Atsunori Ogawa, Marc Delcroix
End-to-end speech summarization (E2E SSum) directly summarizes input speech into easy-to-read short sentences with a single model.
2 code implementations • 16 Nov 2021 • Takatomo Kano, Atsunori Ogawa, Marc Delcroix, Shinji Watanabe
We propose a cascade speech summarization model that is robust to ASR errors and that exploits multiple hypotheses generated by ASR to attenuate the effect of ASR errors on the summary.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 10 Nov 2020 • Katsuhito Sudoh, Takatomo Kano, Sashi Novitasari, Tomoya Yanagita, Sakriani Sakti, Satoshi Nakamura
This paper presents a newly developed, simultaneous neural speech-to-speech translation system and its evaluation.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +6
no code implementations • 13 Feb 2018 • Takatomo Kano, Sakriani Sakti, Satoshi Nakamura
Sequence-to-sequence attentional-based neural network architectures have been shown to provide a powerful model for machine translation and speech recognition.