Analysing the Language of Neural Audio Codecs
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918133671919616 |
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| author | Park, Joonyong Takamichi, Shinnosuke Chan, David M. Kando, Shunsuke Saito, Yuki Saruwatari, Hiroshi |
| author_facet | Park, Joonyong Takamichi, Shinnosuke Chan, David M. Kando, Shunsuke Saito, Yuki Saruwatari, Hiroshi |
| contents | This study presents a comparative analysis of the statistical and linguistic properties of neural audio codecs (NACs). We investigate discrete speech tokens produced by various NAC models, examining their adherence to linguistic statistical laws such as Zipf's law and Heaps' law, as well as their entropy and redundancy. To assess how these token-level properties relate to semantic and acoustic preservation in synthesized speech, we evaluate intelligibility using error rates of automatic speech recognition, and quality using the UTMOS score. Our results reveal that NAC tokens, particularly 3-grams, exhibit language-like statistical patterns. Moreover, these properties, together with measures of information content, are found to correlate with improved performances in speech recognition and resynthesis tasks. These findings offer insights into the structure of NAC token sequences and inform the design of more effective generative speech models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01390 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analysing the Language of Neural Audio Codecs Park, Joonyong Takamichi, Shinnosuke Chan, David M. Kando, Shunsuke Saito, Yuki Saruwatari, Hiroshi Computation and Language Audio and Speech Processing This study presents a comparative analysis of the statistical and linguistic properties of neural audio codecs (NACs). We investigate discrete speech tokens produced by various NAC models, examining their adherence to linguistic statistical laws such as Zipf's law and Heaps' law, as well as their entropy and redundancy. To assess how these token-level properties relate to semantic and acoustic preservation in synthesized speech, we evaluate intelligibility using error rates of automatic speech recognition, and quality using the UTMOS score. Our results reveal that NAC tokens, particularly 3-grams, exhibit language-like statistical patterns. Moreover, these properties, together with measures of information content, are found to correlate with improved performances in speech recognition and resynthesis tasks. These findings offer insights into the structure of NAC token sequences and inform the design of more effective generative speech models. |
| title | Analysing the Language of Neural Audio Codecs |
| topic | Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.01390 |