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

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ESPnet: End-to-End Speech Processing Toolkit

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classification 💻 cs.CL
keywords espnetspeechprocessingend-to-endmajoropenotherplatform
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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.

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  1. 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.