基于语音合成语料库自动评分的可控制好感度的语音转换
Voice Conversion for Likability Control via Automated Rating of Speech Synthesis Corpora
AI总结：
本文提出一种语音转换方法，通过训练好感度预测器自动标注大型语音合成语料库，在保留说话人身份和语言内容的同时有效控制语音好感度，实验验证了其有效性。
摘要：
感知到的语音好感度在伴侣选择和广告等多种社会互动中起着至关重要的作用。一个能够为目标受众提供参考性好感语音样本的系统，可以使用户调整自己的说话风格和语音质量，从而促进更顺畅的沟通。为此，我们提出了一种语音转换方法，该方法在保留说话人身份和语言内容的同时控制输入语音的好感度。为了提高训练数据的可扩展性，我们在现有的语音好感度数据集上训练了一个好感度预测器，并利用它自动为一个大型语音合成语料库标注好感度评分。实验评估表明，预测器的输出与人工提供的好感度评分之间存在显著相关性。主观和客观评估进一步证明，所提出的方法能够有效控制语音好感度，同时保留说话人身份和语言内容。
英文摘要：
Perceived voice likability plays a crucial role in various social interactions, such as partner selection and advertising. A system that provides reference likable voice samples tailored to target audiences would enable users to adjust their speaking style and voice quality, facilitating smoother communication. To this end, we propose a voice conversion method that controls the likability of input speech while preserving both speaker identity and linguistic content. To improve training data scalability, we train a likability predictor on an existing voice likability dataset and employ it to automatically annotate a large speech synthesis corpus with likability ratings. Experimental evaluations reveal a significant correlation between the predictor's outputs and human-provided likability ratings. Subjective and objective evaluations further demonstrate that the proposed approach effectively controls voice likability while preserving both speaker identity and linguistic content.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
