6 citations · 7 across the 6 of their papers we have counts for
5 papers
Remix-cycle-consistent Learning on Adversarially Learned Separator for Accurate and Stable Unsupervised Speech Separation
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. Generative…
An Investigation of Enhancing CTC Model for Triggered Attention-based Streaming ASR
Huaibo Zhao, Yosuke Higuchi, Tetsuji Ogawa +1
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 tha…
Exploring Effectiveness of Inter-Microtask Qualification Tests in Crowdsourcing
Masaya Morinaga, Susumu Saito, Teppei Nakano +2
Qualification tests in crowdsourcing are often used to pre-filter workers by measuring their ability in executing microtasks.While creating qualification tests for each task type i…
Improved Mask-CTC for Non-Autoregressive End-to-End ASR
Yosuke Higuchi, Hirofumi Inaguma, Shinji Watanabe +2
For real-world deployment of automatic speech recognition (ASR), the system is desired to be capable of fast inference while relieving the requirement of computational resources. T…
Mask CTC: Non-Autoregressive End-to-End ASR with CTC and Mask Predict
Yosuke Higuchi, Shinji Watanabe, Nanxin Chen +2
We present Mask CTC, a novel non-autoregressive end-to-end automatic speech recognition (ASR) framework, which generates a sequence by refining outputs of the connectionist tempora…