eCat: An End-to-End Model for Multi-Speaker TTS & Many-to-Many Fine-Grained Prosody Transfer

Ammar Abbas; Sri Karlapati; Bastian Schnell; Penny Karanasou; Marcel Granero Moya; Amith Nagaraj; Ayman Boustati; Nicole Peinelt; Alexis Moinet; Thomas Drugman

arXiv · 2306.11327

eCat: An End-to-End Model for Multi-Speaker TTS & Many-to-Many Fine-Grained Prosody Transfer

Ammar Abbas·Sri Karlapati·Bastian Schnell·Penny Karanasou·Marcel Granero Moya·Amith Nagaraj·Ayman Boustati·Nicole Peinelt·Alexis Moinet·Thomas Drugman

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Abstract

We present eCat, a novel end-to-end multispeaker model capable of: a) generating long-context speech with expressive and contextually appropriate prosody, and b) performing fine-grained prosody transfer between any pair of seen speakers. eCat is trained using a two-stage training approach. In Stage I, the model learns speaker-independent word-level prosody representations in an end-to-end fashion from speech. In Stage II, we learn to predict the prosody representations using the contextual information available in text. We compare eCat to CopyCat2, a model capable of both fine-grained prosody transfer (FPT) and multi-speaker TTS. We show that eCat statistically significantly reduces the gap in naturalness between CopyCat2 and human recordings by an average of 46.7% across 2 languages, 3 locales, and 7 speakers, along with better target-speaker similarity in FPT. We also compare eCat to VITS, and show a statistically significant preference.

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Ammar Abbas, Sri Karlapati, Bastian Schnell, Penny Karanasou, Marcel Granero Moya, Amith Nagaraj, Ayman Boustati, Nicole Peinelt, Alexis Moinet, Thomas Drugman. 2023-06-20. eCat: An End-to-End Model for Multi-Speaker TTS & Many-to-Many Fine-Grained Prosody Transfer. https://arxiv.org/abs/2306.11327

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