Zero-shot multi-speaker text-to-speech (TTS) systems rely on speaker embeddings to synthesize speech in the voice of an unseen speaker, using only a short reference utterance. While many speaker embeddings have been developed for speaker recognition, their relative effectiveness in zero-shot TTS remains underexplored. In this work, we employ a YourTTS-based TTS system to compare three different speaker encoders - YourTTS's original H/ASP encoder, x-vector embeddings, and ECAPA-TDNN embeddings - within an otherwise fixed zero-shot TTS framework. All models were trained on the same dataset of Czech read speech and evaluated on 24 out-of-domain target speakers using both subjective and objective methods. The subjective evaluation was conducted via a listening test focused on speaker similarity, while the objective evaluation measured cosine distances between speaker embeddings extracted from synthesized and real utterances. Across both evaluations, the original H/ASP encoder consistently outperformed the alternatives, with ECAPA-TDNN showing better results than x-vectors. These findings suggest that, despite the popularity of ECAPA-TDNN in speaker recognition, it does not necessarily offer improvements for speaker similarity in zero-shot TTS in this configuration. Our study highlights the importance of empirical evaluation when reusing speaker recognition embeddings in TTS and provides a framework for additional future comparisons.

翻译：零样本多说话人文本转语音系统依赖说话人嵌入，仅通过简短参考语音即可合成未见说话人音色的语音。尽管已开发出多种用于说话人识别的说话人嵌入，但它们在零样本TTS中的相对有效性仍未得到充分探究。本研究采用基于YourTTS的TTS系统，在保持其他框架固定的条件下，比较了三种不同的说话人编码器：YourTTS原有的H/ASP编码器、x-vector嵌入和ECAPA-TDNN嵌入。所有模型均在相同的捷克语朗读语音数据集上训练，并采用主客观方法对24个域外目标说话人进行评估。主观评估通过专注于说话人相似度的听力测试进行，客观评估则通过计算合成语音与真实语音所提取说话人嵌入间的余弦距离来衡量。两项评估结果均表明，原有的H/ASP编码器持续优于其他方案，而ECAPA-TDNN的表现优于x-vector。这些发现提示，尽管ECAPA-TDNN在说话人识别领域广受欢迎，但在当前配置下并未对零样本TTS的说话人相似度带来必然提升。本研究强调了在TTS中复用说话人识别嵌入时实证评估的重要性，并为未来进一步的对比研究提供了框架。