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title
ProsoSpeech: Enhancing Prosody With Quantized Vector Pre-training in Text-to-Speech
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ProsoSpeech: Enhancing Prosody With Quantized Vector Pre-training in Text-to-Speech
author
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2026-02-27 12:51:09
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ProsoSpeech: Enhancing Prosody With Quantized Vector Pre-training in Text-to-Speech Skip to the content. ProsoSpeech: Enhancing Prosody With Quantized Vector Pre-training in Text-to-Speech Expressive text-to-speech (TTS) has become a hot research topic recently, mainly focusing on modeling prosody in speech. Prosody modeling has several challenges: 1) the extracted pitch used in previous prosody modeling works have inevitable errors, which hurts the prosody modeling; 2) different attributes of prosody (e.g., pitch, duration and energy) are dependent on each other and produce the nature prosody together; and 3) due to high variability of prosody and the limited amount of high-quality data for TTS training, the distribution of prosody cannot be fully shaped. To tackle these issues, we propose ProsoSpeech, which enhances the prosody using quantized latent vectors pre-trained on large-scale unpaired and low-quality text and speech data. Specifically, we first introduce a word-level p...
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