arxiv: 1804.00015 · v1 · pith:L4YFKAUMnew · submitted 2018-03-30 · 💻 cs.CL

ESPnet: End-to-End Speech Processing Toolkit

Shinji Watanabe , Takaaki Hori , Shigeki Karita , Tomoki Hayashi , Jiro Nishitoba , Yuya Unno , Nelson Enrique Yalta Soplin , Jahn Heymann

show 4 more authors

Matthew Wiesner Nanxin Chen Adithya Renduchintala Tsubasa Ochiai

This is my paper

classification 💻 cs.CL

keywords espnetspeechprocessingend-to-endmajoropenotherplatform

0 comments

read the original abstract

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.

This paper has not been read by Pith yet.

discussion (0)

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

TokenChain: A Discrete Speech Chain via Semantic Token Modeling
eess.AS 2025-10 unverdicted novelty 7.0

TokenChain demonstrates that a discrete semantic-token interface can sustain effective chain learning between ASR and TTS, yielding faster convergence and lower error rates on LibriSpeech and TED-LIUM.
LLMs and Speech: Integration vs. Combination
eess.AS 2026-03 unverdicted novelty 4.0

Tight integration of acoustic models with LLMs for ASR is ablated against shallow fusion across label units, fine-tuning strategies, LLM sizes, and joint CTC decoding to mitigate hallucinations.