Probing neural audio codecs for distinctions among English nuclear tunes
State-of-the-art spoken dialogue models (Défossez et al. 2024; Schalkwyk et al. 2025) use neural audio codecs to "tokenize" audio signals into a lower-frequency stream of vectorial latent representations, each quantized using a hierarchy of vector codebooks. A transformer layer allows these representations to reflect some time- and context-dependent patterns. We train probes on labeled audio data from Cole et al. (2023) to test whether the pitch trajectories that characterize English phrase-final (nuclear) intonational tunes are among these patterns. Results: Linear probes trained on the unquantized latents or some of the associated codewords yield above-chance accuracy in distinguishing ...
| Authors | Juan Pablo Vigneaux, Jennifer Cole |
|---|---|
| Primary category | cs.SD |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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