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About this paper
Marie Kunešová, Zdeněk Hanzlíček, Jindřich Matoušek
An exploration of ECAPA-TDNN and x-vector speaker representations in zero-shot multi-speaker TTS revealed that the baseline H/ASP encoder, integral to the YourTTS system, consistently achieved higher speaker similarity than the speaker recognition-focused embeddings. Both human evaluations and objective cosine distance metrics confirmed the superior performance of the H/ASP encoder, followed by ECAPA-TDNN and then x-vector.
Key insights
- Speaker embeddings highly successful in speaker recognition (e.g., ECAPA-TDNN) do not automatically translate to superior performance in speaker similarity for zero-shot TTS in a direct replacement scenario.
- The study highlights that optimal characteristics for discriminating between speakers (recognition) may differ from those required for generating a highly similar voice (synthesis).
- The H/ASP encoder's approach, which involves averaging embeddings from short, spaced audio segments, potentially contributes to its superior performance in TTS speaker similarity compared to full-utterance processing by x-vector and ECAPA-TDNN.
Summary by alphaXiv.
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