788 citations · 887 across the 15 of their papers we have counts for
15 papers
ReverbMiipher: Generative Speech Restoration meets Reverberation Characteristics Controllability
Wataru Nakata, Yuma Koizumi, Shigeki Karita +5
Reverberation encodes spatial information regarding the acoustic source environment, yet traditional Speech Restoration (SR) usually completely removes reverberation. We propose Re…
Miipher-2: A Universal Speech Restoration Model for Million-Hour Scale Data Restoration
Shigeki Karita, Yuma Koizumi, Heiga Zen +3
Training data cleaning is a new application for generative model-based speech restoration (SR). This paper introduces Miipher-2, an SR model designed for million-hour scale data, f…
FLEURS-R: A Restored Multilingual Speech Corpus for Generation Tasks
Min Ma, Yuma Koizumi, Shigeki Karita +4
This paper introduces FLEURS-R, a speech restoration applied version of the Few-shot Learning Evaluation of Universal Representations of Speech (FLEURS) corpus. FLEURS-R maintains…
Lenient Evaluation of Japanese Speech Recognition: Modeling Naturally Occurring Spelling Inconsistency
Shigeki Karita, Richard Sproat, Haruko Ishikawa
Word error rate (WER) and character error rate (CER) are standard metrics in Speech Recognition (ASR), but one problem has always been alternative spellings: If one's system transc…
LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus
Yuma Koizumi, Heiga Zen, Shigeki Karita +7
This paper introduces a new speech dataset called ``LibriTTS-R'' designed for text-to-speech (TTS) use. It is derived by applying speech restoration to the LibriTTS corpus, which c…
Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations
Yuma Koizumi, Heiga Zen, Shigeki Karita +7
Speech restoration (SR) is a task of converting degraded speech signals into high-quality ones. In this study, we propose a robust SR model called Miipher, and apply Miipher to a n…
Knowledge Transfer from Large-scale Pretrained Language Models to End-to-end Speech Recognizers
Yotaro Kubo, Shigeki Karita, Michiel Bacchiani
End-to-end speech recognition is a promising technology for enabling compact automatic speech recognition (ASR) systems since it can unify the acoustic and language model into a si…
SNRi Target Training for Joint Speech Enhancement and Recognition
Yuma Koizumi, Shigeki Karita, Arun Narayanan +2
Speech enhancement (SE) is used as a frontend in speech applications including automatic speech recognition (ASR) and telecommunication. A difficulty in using the SE frontend is th…
A Comparative Study on Neural Architectures and Training Methods for Japanese Speech Recognition
Shigeki Karita, Yotaro Kubo, Michiel Adriaan Unico Bacchiani +1
End-to-end (E2E) modeling is advantageous for automatic speech recognition (ASR) especially for Japanese since word-based tokenization of Japanese is not trivial, and E2E modeling…
DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement
Yuma Koizumi, Shigeki Karita, Scott Wisdom +4
Single-channel speech enhancement (SE) is an important task in speech processing. A widely used framework combines an analysis/synthesis filterbank with a mask prediction network,…
The 2020 ESPnet update: new features, broadened applications, performance improvements, and future plans
Shinji Watanabe, Florian Boyer, Xuankai Chang +12
This paper describes the recent development of ESPnet (https://github.com/espnet/espnet), an end-to-end speech processing toolkit. This project was initiated in December 2017 to ma…
Unsupervised Learning of Disentangled Speech Content and Style Representation
Andros Tjandra, Ruoming Pang, Yu Zhang +1
We present an approach for unsupervised learning of speech representation disentangling contents and styles. Our model consists of: (1) a local encoder that captures per-frame info…
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…
A Comparative Study on Transformer vs RNN in Speech Applications
Shigeki Karita, Nanxin Chen, Tomoki Hayashi +10
Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS…
ESPnet: End-to-End Speech Processing Toolkit
Shinji Watanabe, Takaaki Hori, Shigeki Karita +9
This paper introduces a new open source platform for end-to-end speech processing named ESPnet. ESPnet mainly focuses on end-to-end automatic speech recognition (ASR), and adopts w…