Details

Autor(en) / Beteiligte

• Guo, Pengcheng
• Boyer, Florian
• Chang, Xuankai
• Hayashi, Tomoki
• Higuchi, Yosuke
• Inaguma, Hirofumi
• Kamo, Naoyuki
• Li, Chenda
• Garcia-Romero, Daniel
• Shi, Jiatong
• Shi, Jing
• Watanabe, Shinji
• Wei, Kun
• Zhang, Wangyou
• Zhang, Yuekai

Titel

Recent Developments on Espnet Toolkit Boosted By Conformer

Ist Teil von

• ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, p.5874-5878

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• In this study, we present recent developments on ESPnet: End-to- End Speech Processing toolkit, which mainly involves a recently proposed architecture called Conformer, Convolution-augmented Transformer. This paper shows the results for a wide range of end- to-end speech processing applications, such as automatic speech recognition (ASR), speech translations (ST), speech separation (SS) and text-to-speech (TTS). Our experiments reveal various training tips and significant performance benefits obtained with the Conformer on different tasks. These results are competitive or even outperform the current state-of-art Transformer models. We are preparing to release all-in-one recipes using open source and publicly available corpora for all the above tasks with pre-trained models. Our aim for this work is to contribute to our research community by reducing the burden of preparing state-of-the-art research environments usually requiring high resources.