no code implementations • 23 Apr 2024 • Tsubasa Ochiai, Kazuma Iwamoto, Marc Delcroix, Rintaro Ikeshita, Hiroshi Sato, Shoko Araki, Shigeru Katagiri
To this end, we propose a novel analysis scheme based on the orthogonal projection-based decomposition of SE errors.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 20 Nov 2023 • Kazuma Iwamoto, Tsubasa Ochiai, Marc Delcroix, Rintaro Ikeshita, Hiroshi Sato, Shoko Araki, Shigeru Katagiri
Jointly training a speech enhancement (SE) front-end and an automatic speech recognition (ASR) back-end has been investigated as a way to mitigate the influence of \emph{processing distortion} generated by single-channel SE on ASR.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 18 Jan 2022 • Kazuma Iwamoto, Tsubasa Ochiai, Marc Delcroix, Rintaro Ikeshita, Hiroshi Sato, Shoko Araki, Shigeru Katagiri
The artifact component is defined as the SE error signal that cannot be represented as a linear combination of speech and noise sources.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 17 Nov 2016 • Tsubasa Ochiai, Shigeki Matsuda, Hideyuki Watanabe, Shigeru Katagiri
We examine the effect of the Group Lasso (gLasso) regularizer in selecting the salient nodes of Deep Neural Network (DNN) hidden layers by applying a DNN-HMM hybrid speech recognizer to TED Talks speech data.