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We present eCat, a novel end-to-end multispeaker model capable of: a)generating long-context speech with expressive and contextually appropriateprosody, and b) performing fine-grained prosody transfer between any pair ofseen speakers. eCat is trained using a two-stage training approach. In Stage I,the model learns speaker-independent word-level prosody representations in anend-to-end fashion from speech. In Stage II, we learn to predict the prosodyrepresentations using the contextual information available in text. We compareeCat to CopyCat2, a model capable of both fine-grained prosody transfer (FPT)and multi-speaker TTS. We show that eCat statistically significantly reducesthe gap in naturalness between CopyCat2 and human recordings by an average of46.7% across 2 languages, 3 locales, and 7 speakers, along with bettertarget-speaker similarity in FPT. We also compare eCat to VITS, and show astatistically significant preference.

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