DelexWav: An Audible Representation of Fine-Grained Prosody

Speech carries both lexical content and prosody. Explicit acoustic features describe selected aspects of prosody, while learned vectors and tokens are heard through a downstream generator. We introduce DelexWav, a learned waveform representation that preserves fine-grained prosody while suppressing recoverable lexical content. DelexWav provides utterance-specific prosodic conditioning for speech synthesis. Its waveform form enables direct listening, acoustic comparison with the original speech, and local editing. We construct fixed-phone targets using phoneme-level conditional-flow TTS and use them as the main supervision for a neural codec that extracts DelexWav directly from speech, without transcripts or phone alignments at inference. On 1,234 LibriSpeech test-clean utterances, DelexWav preserves pitch and energy variation with correlations of 0.96 and 0.85. A phone recognizer trained on DelexWav recovers no complete source words. The English-trained model also preserves prosodic variation in cross-lingual tests without retraining. Speech synthesis experiments show that DelexWav provides fine-grained prosodic conditioning, with local edits producing corresponding changes in the generated speech. Samples are provided in the supplementary material.