标题： 逐层分析多语言合成语音的质量 显示英文标题

标题： Layer-wise Analysis for Quality of Multilingual Synthesized Speech

摘要： 虽然监督质量预测器在合成语音中已显示出与人类评分的强相关性，但它们对领域内标记训练数据的要求阻碍了其在新领域的泛化能力。 基于预训练自监督学习（SSL）模型和自动语音识别（ASR）模型的无监督方法是一种有前景的替代方案；然而，关于这些模型如何编码语音质量的信息了解甚少。 为了更好地理解不同方面的语音质量在多语言环境中的编码方式，我们基于参考建模进行了多语言预训练语音模型的逐层分析。 我们发现，从早期SSL层提取的特征与合成语音的人类评分有关，而ASR模型的后期层可以预测非神经系统的质量以及可懂度。 我们还证明了使用匹配良好的参考数据的重要性。 显示英文摘要

摘要： While supervised quality predictors for synthesized speech have demonstrated strong correlations with human ratings, their requirement for in-domain labeled training data hinders their generalization ability to new domains. Unsupervised approaches based on pretrained self-supervised learning (SSL) based models and automatic speech recognition (ASR) models are a promising alternative; however, little is known about how these models encode information about speech quality. Towards the goal of better understanding how different aspects of speech quality are encoded in a multilingual setting, we present a layer-wise analysis of multilingual pretrained speech models based on reference modeling. We find that features extracted from early SSL layers show correlations with human ratings of synthesized speech, and later layers of ASR models can predict quality of non-neural systems as well as intelligibility. We also demonstrate the importance of using well-matched reference data.