40 citations · 82 across the 23 of their papers we have counts for
6 papers · 1 filter
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…
A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation
Yosuke Higuchi, Nanxin Chen, Yuya Fujita +6
Non-autoregressive (NAR) models simultaneously generate multiple outputs in a sequence, which significantly reduces the inference speed at the cost of accuracy drop compared to aut…
Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy
Yosuke Higuchi, Niko Moritz, Jonathan Le Roux +1
Pseudo-labeling (PL), a semi-supervised learning (SSL) method where a seed model performs self-training using pseudo-labels generated from untranscribed speech, has been shown to e…
Hierarchical Conditional End-to-End ASR with CTC and Multi-Granular Subword Units
Yosuke Higuchi, Keita Karube, Tetsuji Ogawa +1
In end-to-end automatic speech recognition (ASR), a model is expected to implicitly learn representations suitable for recognizing a word-level sequence. However, the huge abstract…
Non-autoregressive End-to-end Speech Translation with Parallel Autoregressive Rescoring
Hirofumi Inaguma, Yosuke Higuchi, Kevin Duh +2
This article describes an efficient end-to-end speech translation (E2E-ST) framework based on non-autoregressive (NAR) models. End-to-end speech translation models have several adv…
Momentum Pseudo-Labeling for Semi-Supervised Speech Recognition
Yosuke Higuchi, Niko Moritz, Jonathan Le Roux +1
Pseudo-labeling (PL) has been shown to be effective in semi-supervised automatic speech recognition (ASR), where a base model is self-trained with pseudo-labels generated from unla…