41 citations · 152 across the 31 of their papers we have counts for
5 papers · 2 filters
Orthros: Non-autoregressive End-to-end Speech Translation with Dual-decoder
Hirofumi Inaguma, Yosuke Higuchi, Kevin Duh +2
Fast inference speed is an important goal towards real-world deployment of speech translation (ST) systems. End-to-end (E2E) models based on the encoder-decoder architecture are mo…
Distilling the Knowledge of BERT for Sequence-to-Sequence ASR
Hayato Futami, Hirofumi Inaguma, Sei Ueno +3
Attention-based sequence-to-sequence (seq2seq) models have achieved promising results in automatic speech recognition (ASR). However, as these models decode in a left-to-right way,…
Minimum Latency Training Strategies for Streaming Sequence-to-Sequence ASR
Hirofumi Inaguma, Yashesh Gaur, Liang Lu +2
Recently, a few novel streaming attention-based sequence-to-sequence (S2S) models have been proposed to perform online speech recognition with linear-time decoding complexity. Howe…
CTC-synchronous Training for Monotonic Attention Model
Hirofumi Inaguma, Masato Mimura, Tatsuya Kawahara
Monotonic chunkwise attention (MoChA) has been studied for the online streaming automatic speech recognition (ASR) based on a sequence-to-sequence framework. In contrast to connect…
ESPnet-ST: All-in-One Speech Translation Toolkit
Hirofumi Inaguma, Shun Kiyono, Kevin Duh +4
We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-to-end speech…