M
Moto Hira
Researcher at Facebook
Publications - 4
Citations - 72
Moto Hira is an academic researcher from Facebook. The author has contributed to research in topics: Speech enhancement & Computer science. The author has an hindex of 2, co-authored 3 publications receiving 14 citations.
Papers
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Proceedings ArticleDOI
ESPnet-SE: End-To-End Speech Enhancement and Separation Toolkit Designed for ASR Integration
Chenda Li,Jing Shi,Wangyou Zhang,Aswin Shanmugam Subramanian,Xuankai Chang,Naoyuki Kamo,Moto Hira,Tomoki Hayashi,Christoph Boeddeker,Zhuo Chen,Shinji Watanabe +10 more
TL;DR: The ESPnet-SE toolkit as mentioned in this paper integrates rich automatic speech recognition related models, resources and systems to support and validate the proposed front-end implementation (i.e. speech enhancement and separation).
Posted Content
ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration
Chenda Li,Jing Shi,Wangyou Zhang,Aswin Shanmugam Subramanian,Xuankai Chang,Naoyuki Kamo,Moto Hira,Tomoki Hayashi,Christoph Boeddeker,Zhuo Chen,Shinji Watanabe +10 more
TL;DR: The design of the toolkit, several important functionalities, especially the speech recognition integration, which differentiates ESPnet-SE from other open source toolkits, and experimental results with major benchmark datasets are described.
Posted Content
TorchAudio: Building Blocks for Audio and Speech Processing
Yao-Yuan Yang,Moto Hira,Zhaoheng Ni,Anjali Chourdia,Artyom Astafurov,Caroline Chen,Ching-Feng Yeh,Christian Puhrsch,David Pollack,Dmitriy Genzel,Donny Greenberg,Edward Z. Yang,Jason Lian,Jay Mahadeokar,Jeff Hwang,Ji Chen,Peter Goldsborough,Prabhat Roy,Sean Narenthiran,Shinji Watanabe,Soumith Chintala,Vincent Quenneville-Bélair,Yangyang Shi +22 more
TL;DR: Torchaudio as discussed by the authors is a set of building blocks for machine learning applications in the audio and speech processing domain that can be easily installed from Python Package Index repository and the source code is publicly available under a BSD-2-Clause License.
Proceedings ArticleDOI
ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit
Brian Yan,Jiatong Shi,Yun Tang,Hirofumi Inaguma,Yifan Peng,Siddharth Dalmia,Peter Pol'ak,Patrick Fernandes,Dan Berrebbi,Tomoki Hayashi,Zhaoheng Ni,Moto Hira,Soumi Maiti,Juan Pino,Shinji Watanabe +14 more
TL;DR: ESPnet-ST-v2 as discussed by the authors is a revamp of the open-source ESPNet-ST toolkit necessitated by the broadening interests of the spoken language translation community.