137 citations · 256 across the 24 of their papers we have counts for
12 papers · 1 filter
Non-autoregressive Error Correction for CTC-based ASR with Phone-conditioned Masked LM
Hayato Futami, Hirofumi Inaguma, Sei Ueno +3
Connectionist temporal classification (CTC) -based models are attractive in automatic speech recognition (ASR) because of their non-autoregressive nature. To take advantage of text…
Distilling the Knowledge of BERT for CTC-based ASR
Hayato Futami, Hirofumi Inaguma, Masato Mimura +2
Connectionist temporal classification (CTC) -based models are attractive because of their fast inference in automatic speech recognition (ASR). Language model (LM) integration appr…
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…
Multi-Referenced Training for Dialogue Response Generation
Tianyu Zhao, Tatsuya Kawahara
In open-domain dialogue response generation, a dialogue context can be continued with diverse responses, and the dialogue models should capture such one-to-many relations. In this…
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,…
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…
Designing Precise and Robust Dialogue Response Evaluators
Tianyu Zhao, Divesh Lala, Tatsuya Kawahara
Automatic dialogue response evaluator has been proposed as an alternative to automated metrics and human evaluation. However, existing automatic evaluators achieve only moderate co…
Multilingual End-to-End Speech Translation
Hirofumi Inaguma, Kevin Duh, Tatsuya Kawahara +1
In this paper, we propose a simple yet effective framework for multilingual end-to-end speech translation (ST), in which speech utterances in source languages are directly translat…
Improving OOV Detection and Resolution with External Language Models in Acoustic-to-Word ASR
Hirofumi Inaguma, Masato Mimura, Shinsuke Sakai +1
Acoustic-to-word (A2W) end-to-end automatic speech recognition (ASR) systems have attracted attention because of an extremely simplified architecture and fast decoding. To alleviat…
Content Word-based Sentence Decoding and Evaluating for Open-domain Neural Response Generation
Tianyu Zhao, Shinsuke Mori, Tatsuya Kawahara
Various encoder-decoder models have been applied to response generation in open-domain dialogs, but a majority of conventional models directly learn a mapping from lexical input to…
Transfer learning of language-independent end-to-end ASR with language model fusion
Hirofumi Inaguma, Jaejin Cho, Murali Karthick Baskar +2
This work explores better adaptation methods to low-resource languages using an external language model (LM) under the framework of transfer learning. We first build a language-ind…