Search Results for author: Tetsuji Ogawa

Found 7 papers, 1 papers with code

Remix-cycle-consistent Learning on Adversarially Learned Separator for Accurate and Stable Unsupervised Speech Separation

no code implementations26 Mar 2022 Kohei Saijo, Tetsuji Ogawa

A new learning algorithm for speech separation networks is designed to explicitly reduce residual noise and artifacts in the separated signal in an unsupervised manner.

Speech Separation

An Investigation of Enhancing CTC Model for Triggered Attention-based Streaming ASR

no code implementations20 Oct 2021 Huaibo Zhao, Yosuke Higuchi, Tetsuji Ogawa, Tetsunori Kobayashi

In the present paper, an attempt is made to combine Mask-CTC and the triggered attention mechanism to construct a streaming end-to-end automatic speech recognition (ASR) system that provides high performance with low latency.

Automatic Speech Recognition

Hierarchical Conditional End-to-End ASR with CTC and Multi-Granular Subword Units

1 code implementation8 Oct 2021 Yosuke Higuchi, Keita Karube, Tetsuji Ogawa, Tetsunori Kobayashi

In this work, to promote the word-level representation learning in end-to-end ASR, we propose a hierarchical conditional model that is based on connectionist temporal classification (CTC).

Automatic Speech Recognition Representation Learning

Improved Mask-CTC for Non-Autoregressive End-to-End ASR

no code implementations26 Oct 2020 Yosuke Higuchi, Hirofumi Inaguma, Shinji Watanabe, Tetsuji Ogawa, Tetsunori Kobayashi

While Mask-CTC achieves remarkably fast inference speed, its recognition performance falls behind that of conventional autoregressive (AR) systems.

Automatic Speech Recognition Translation

Mask CTC: Non-Autoregressive End-to-End ASR with CTC and Mask Predict

no code implementations18 May 2020 Yosuke Higuchi, Shinji Watanabe, Nanxin Chen, Tetsuji Ogawa, Tetsunori Kobayashi

In this work, Mask CTC model is trained using a Transformer encoder-decoder with joint training of mask prediction and CTC.

Audio and Speech Processing Sound

Block-wise Scrambled Image Recognition Using Adaptation Network

no code implementations21 Jan 2020 Koki Madono, Masayuki Tanaka, Masaki Onishi, Tetsuji Ogawa

In this study, a perceptually hidden object-recognition method is investigated to generate secure images recognizable by humans but not machines.

Image Classification Object Recognition

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