137 citations · 256 across the 24 of their papers we have counts for
28 papers
Diffusion-Based Speech Enhancement with Joint Generative and Predictive Decoders
Hao Shi, Kazuki Shimada, Masato Hirano +6
Diffusion-based generative speech enhancement (SE) has recently received attention, but reverse diffusion remains time-consuming. One solution is to initialize the reverse diffusio…
Time-domain Speech Enhancement Assisted by Multi-resolution Frequency Encoder and Decoder
Hao Shi, Masato Mimura, Longbiao Wang +2
Time-domain speech enhancement (SE) has recently been intensively investigated. Among recent works, DEMUCS introduces multi-resolution STFT loss to enhance performance. However, so…
Alzheimer's Dementia Detection through Spontaneous Dialogue with Proactive Robotic Listeners
Yuanchao Li, Catherine Lai, Divesh Lala +2
As the aging of society continues to accelerate, Alzheimer's Disease (AD) has received more and more attention from not only medical but also other fields, such as computer science…
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…
End-to-end Speech-to-Punctuated-Text Recognition
Jumon Nozaki, Tatsuya Kawahara, Kenkichi Ishizuka +1
Conventional automatic speech recognition systems do not produce punctuation marks which are important for the readability of the speech recognition results. They are also needed f…
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…
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…
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…
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…
End-to-end Music-mixed Speech Recognition
Jeongwoo Woo, Masato Mimura, Kazuyoshi Yoshii +1
Automatic speech recognition (ASR) in multimedia content is one of the promising applications, but speech data in this kind of content are frequently mixed with background music, w…
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,…
Enhancing Monotonic Multihead Attention for Streaming ASR
Hirofumi Inaguma, Masato Mimura, Tatsuya Kawahara
We investigate a monotonic multihead attention (MMA) by extending hard monotonic attention to Transformer-based automatic speech recognition (ASR) for online streaming applications…
Generative Adversarial Training Data Adaptation for Very Low-resource Automatic Speech Recognition
Kohei Matsuura, Masato Mimura, Shinsuke Sakai +1
It is important to transcribe and archive speech data of endangered languages for preserving heritages of verbal culture and automatic speech recognition (ASR) is a powerful tool t…
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…
End-to-end speech-to-dialog-act recognition
Viet-Trung Dang, Tianyu Zhao, Sei Ueno +2
Spoken language understanding, which extracts intents and/or semantic concepts in utterances, is conventionally formulated as a post-processing of automatic speech recognition. It…
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…
Effective Incorporation of Speaker Information in Utterance Encoding in Dialog
Tianyu Zhao, Tatsuya Kawahara
In dialog studies, we often encode a dialog using a hierarchical encoder where each utterance is converted into an utterance vector, and then a sequence of utterance vectors is con…
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…