788 citations · 967 across the 40 of their papers we have counts for
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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…
ASR Rescoring and Confidence Estimation with ELECTRA
Hayato Futami, Hirofumi Inaguma, Masato Mimura +2
In automatic speech recognition (ASR) rescoring, the hypothesis with the fewest errors should be selected from the n-best list using a language model (LM). However, LMs are usually…
Fast-MD: Fast Multi-Decoder End-to-End Speech Translation with Non-Autoregressive Hidden Intermediates
Hirofumi Inaguma, Siddharth Dalmia, Brian Yan +1
The multi-decoder (MD) end-to-end speech translation model has demonstrated high translation quality by searching for better intermediate automatic speech recognition (ASR) decoder…
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…
VAD-free Streaming Hybrid CTC/Attention ASR for Unsegmented Recording
Hirofumi Inaguma, Tatsuya Kawahara
In this work, we propose novel decoding algorithms to enable streaming automatic speech recognition (ASR) on unsegmented long-form recordings without voice activity detection (VAD)…
StableEmit: Selection Probability Discount for Reducing Emission Latency of Streaming Monotonic Attention ASR
Hirofumi Inaguma, Tatsuya Kawahara
While attention-based encoder-decoder (AED) models have been successfully extended to the online variants for streaming automatic speech recognition (ASR), such as monotonic chunkw…
ESPnet-ST IWSLT 2021 Offline Speech Translation System
Hirofumi Inaguma, Brian Yan, Siddharth Dalmia +4
This paper describes the ESPnet-ST group's IWSLT 2021 submission in the offline speech translation track. This year we made various efforts on training data, architecture, and audi…
Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation
Hirofumi Inaguma, Tatsuya Kawahara, Shinji Watanabe
A conventional approach to improving the performance of end-to-end speech translation (E2E-ST) models is to leverage the source transcription via pre-training and joint training wi…
Alignment Knowledge Distillation for Online Streaming Attention-based Speech Recognition
Hirofumi Inaguma, Tatsuya Kawahara
This article describes an efficient training method for online streaming attention-based encoder-decoder (AED) automatic speech recognition (ASR) systems. AED models have achieved…