Share your thoughts, 1 month free Claude Pro on usSee more
WorkDL logo mark

ESPnet: End-to-End Speech Processing Toolkit

About

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 widely-used dynamic neural network toolkits, Chainer and PyTorch, as a main deep learning engine. ESPnet also follows the Kaldi ASR toolkit style for data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments. This paper explains a major architecture of this software platform, several important functionalities, which differentiate ESPnet from other open source ASR toolkits, and experimental results with major ASR benchmarks.

Shinji Watanabe, Takaaki Hori, Shigeki Karita, Tomoki Hayashi, Jiro Nishitoba, Yuya Unno, Nelson Enrique Yalta Soplin, Jahn Heymann, Matthew Wiesner, Nanxin Chen, Adithya Renduchintala, Tsubasa Ochiai• 2018

Related benchmarks

TaskDatasetResultRank
Automatic Speech RecognitionLibriSpeech (test-other)
WER5.3
1447
Automatic Speech RecognitionLibriSpeech clean (test)
WER2
1410
Automatic Speech RecognitionLibriSpeech (dev-other)
WER5.2
535
Automatic Speech RecognitionLibriSpeech (dev-clean)
WER (%)1.9
376
Automatic Speech RecognitionAISHELL-1 (test)
CER4.5
177
Speech RecognitionWSJ (92-eval)
WER8.9
131
Automatic Speech RecognitionWenetSpeech Meeting (test)
CER15.9
78
Automatic Speech RecognitionAISHELL-1 (dev)
CER4.2
66
Automatic Speech RecognitionWenetSpeech Net (test)
CER8.9
57
Automatic Speech RecognitionGigaSpeech (test)
WER10.5
55
Showing 10 of 25 rows

Other info

Follow for update