基于语言知识的填充停顿个性化自发语音合成实证研究
Empirical Study Incorporating Linguistic Knowledge on Filled Pauses for Personalized Spontaneous Speech Synthesis
AI总结：
本文通过实证研究探讨个性化自发语音合成中的填充停顿问题，提出结合非个性化外部填充停顿预测器的合成方法，揭示填充停顿位置与词语分别影响自然度和个体性的关键规律。
摘要：
我们提出了一项基于语言知识的个性化自发语音合成综合实证研究。随着朗读式语音合成语音克隆技术的出现，需要一种新的面向类人自发语音合成的语音克隆范式。因此，我们专注于个性化自发语音合成，能够同时克隆个体的音色和言语不流畅特征。具体而言，我们研究填充停顿——言语不流畅的主要来源之一，心理学和语言学研究表明填充停顿在语音生成和沟通中具有重要作用。为了对比评估个性化填充停顿插入方法和非个性化填充停顿预测方法，我们开发了一种语音合成方法，该方法采用由多说话人语料库训练的非个性化外部填充停顿预测器。实验结果揭示了填充停顿的位置-词语纠缠现象，即在合成语音评估中，精确预测位置对于自然度是必要的，而精确预测词语对于个体性是必要的。
英文摘要：
We present a comprehensive empirical study for personalized spontaneous speech synthesis on the basis of linguistic knowledge. With the advent of voice cloning for reading-style speech synthesis, a new voice cloning paradigm for human-like and spontaneous speech synthesis is required. We, therefore, focus on personalized spontaneous speech synthesis that can clone both the individual's voice timbre and speech disfluency. Specifically, we deal with filled pauses, a major source of speech disfluency, which is known to play an important role in speech generation and communication in psychology and linguistics. To comparatively evaluate personalized filled pause insertion and non-personalized filled pause prediction methods, we developed a speech synthesis method with a non-personalized external filled pause predictor trained with a multi-speaker corpus. The results clarify the position-word entanglement of filled pauses, i.e., the necessity of precisely predicting positions for naturalness and the necessity of precisely predicting words for individuality on the evaluation of synthesized speech.
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