Representing Speech Through Autoregressive Prediction of Cochlear Tokens
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909738097180672 |
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| author | Tuckute, Greta Kotar, Klemen Fedorenko, Evelina Yamins, Daniel L. K. |
| author_facet | Tuckute, Greta Kotar, Klemen Fedorenko, Evelina Yamins, Daniel L. K. |
| contents | We introduce AuriStream, a biologically inspired model for encoding speech via a two-stage framework inspired by the human auditory processing hierarchy. The first stage transforms raw audio into a time-frequency representation based on the human cochlea, from which we extract discrete \textbf{cochlear tokens}. The second stage applies an autoregressive sequence model over the cochlear tokens. AuriStream learns meaningful phoneme and word representations, and state-of-the-art lexical semantics. AuriStream shows competitive performance on diverse downstream SUPERB speech tasks. Complementing AuriStream's strong representational capabilities, it generates continuations of audio which can be visualized in a spectrogram space and decoded back into audio, providing insights into the model's predictions. In summary, we present a two-stage framework for speech representation learning to advance the development of more human-like models that efficiently handle a range of speech-based tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_11598 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Representing Speech Through Autoregressive Prediction of Cochlear Tokens Tuckute, Greta Kotar, Klemen Fedorenko, Evelina Yamins, Daniel L. K. Computation and Language Sound Audio and Speech Processing We introduce AuriStream, a biologically inspired model for encoding speech via a two-stage framework inspired by the human auditory processing hierarchy. The first stage transforms raw audio into a time-frequency representation based on the human cochlea, from which we extract discrete \textbf{cochlear tokens}. The second stage applies an autoregressive sequence model over the cochlear tokens. AuriStream learns meaningful phoneme and word representations, and state-of-the-art lexical semantics. AuriStream shows competitive performance on diverse downstream SUPERB speech tasks. Complementing AuriStream's strong representational capabilities, it generates continuations of audio which can be visualized in a spectrogram space and decoded back into audio, providing insights into the model's predictions. In summary, we present a two-stage framework for speech representation learning to advance the development of more human-like models that efficiently handle a range of speech-based tasks. |
| title | Representing Speech Through Autoregressive Prediction of Cochlear Tokens |
| topic | Computation and Language Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2508.11598 |