Can Prosodic Style Be Inferred from Text Alone? Evidence from Unsupervised Acoustic Clusters

Title:Can Prosodic Style Be Inferred from Text Alone? Evidence from Unsupervised Acoustic Clusters

Abstract:Much of expressive text-to-speech research rests on an untested assumption that written text carries enough information to select an appropriate prosodic style for its delivery. Text-predicted style models improve listener preference, and expressive-appropriateness evaluation presupposes that context constrains style, yet neither measures the assumption itself. This paper tests it as a falsifiable hypothesis against style labels derived from acoustics alone. For each of six speakers in a 1,200-hour conversational corpus, utterances are clustered in the spaces of five speech models, including a prosody-only control, and the cluster of held-out utterances is predicted from twelve text embedding models. Three controls are applied: utterance length is erased from the speech embeddings; accuracy is scored against the majority-class floor of unbalanced clusters rather than uniform chance; and a bag-of-words baseline measures word identity alone. Text predicts the cluster above that floor for all six speakers (+0.111 top-3 accuracy), but bag-of-words achieves three quarters of this. Sentence embeddings add only +0.026, largest for encoders not trained for sentence semantics and reversed by tree-based probes for all others. Acoustic clusters are not compact in text embedding space in any of 360 configurations. The prosody-only space weakens the association for five speakers, but not for the speaker showing it most strongly. Text thus informs these delivery clusters mainly through word choice, whether as a cue to prosody or as a marker of topic and recording situation, and reference-free style selection cannot assume more.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2610.05575 [cs.CL]

(or arXiv:2610.05575v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.05575 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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