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Speech Recognition On Wenetspeech
Metrics
Character Error Rate (CER)
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| Espnet | 9.7 | WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition | |
| Kaldi | 9.07 | WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition | |
| Wenet | 8.88 | WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition | |
| Conformer-MoE (16e) | 7.67 | 3M: Multi-loss, Multi-path and Multi-level Neural Networks for speech recognition | |
| Conformer-MoE (32e) | 7.49 | 3M: Multi-loss, Multi-path and Multi-level Neural Networks for speech recognition | |
| Zipformer+pruned transducer (no external language model) | 7.29 | Zipformer: A faster and better encoder for automatic speech recognition | |
| Conformer-MoE (64e) | 7.19 | 3M: Multi-loss, Multi-path and Multi-level Neural Networks for speech recognition | |
| Paraformer-large | 6.97 | FunASR: A Fundamental End-to-End Speech Recognition Toolkit |
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