PERL: Pinyin Enhanced Rephrasing Language Model for Chinese ASR N-best Error Correction
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866908550533480448 |
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| author | Liang, Junhong Zhang, Bojun |
| author_facet | Liang, Junhong Zhang, Bojun |
| contents | Existing Chinese ASR correction methods have not effectively utilized Pinyin information, a unique feature of the Chinese language. In this study, we address this gap by proposing a \textbf{P}inyin \textbf{E}nhanced \textbf{R}ephrasing \textbf{L}anguage model (PERL) pipeline, designed explicitly for N-best correction scenarios. We conduct experiments on the Aishell-1 dataset and our newly proposed DoAD dataset. The results show that our approach outperforms baseline methods, achieving a 29.11\% reduction in Character Error Rate on Aishell-1 and around 70\% CER reduction on domain-specific datasets. PERL predicts the correct length of the output, leveraging the Pinyin information, which is embedded with a semantic model to perform phonetically similar corrections. Extensive experiments demonstrate the effectiveness of correcting wrong characters using N-best output and the low latency of our model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03230 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PERL: Pinyin Enhanced Rephrasing Language Model for Chinese ASR N-best Error Correction Liang, Junhong Zhang, Bojun Computation and Language Existing Chinese ASR correction methods have not effectively utilized Pinyin information, a unique feature of the Chinese language. In this study, we address this gap by proposing a \textbf{P}inyin \textbf{E}nhanced \textbf{R}ephrasing \textbf{L}anguage model (PERL) pipeline, designed explicitly for N-best correction scenarios. We conduct experiments on the Aishell-1 dataset and our newly proposed DoAD dataset. The results show that our approach outperforms baseline methods, achieving a 29.11\% reduction in Character Error Rate on Aishell-1 and around 70\% CER reduction on domain-specific datasets. PERL predicts the correct length of the output, leveraging the Pinyin information, which is embedded with a semantic model to perform phonetically similar corrections. Extensive experiments demonstrate the effectiveness of correcting wrong characters using N-best output and the low latency of our model. |
| title | PERL: Pinyin Enhanced Rephrasing Language Model for Chinese ASR N-best Error Correction |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.03230 |