基于隐式和显式声学特征条件的Lombard发音风格语音转换
Voice Conversion for Lombard Speaking Style with Implicit and Explicit Acoustic Feature Conditioning
AI总结：
本文研究Lombard发音风格迁移，提出在语音转换中采用隐式声学特征条件策略，在转换说话人身份的同时保留Lombard风格属性，实现了与显式条件相当的可懂度提升并保持了说话人相似度。
摘要：
Lombard发音风格的文本转语音（TTS）系统可以提高语音的整体可懂度，适用于听力损失和嘈杂环境。然而，训练这些模型需要大量数据，且由于说话人和噪声的变异性以及令人疲劳的录音条件，Lombard效应的录制极具挑战性。在缺乏目标说话人在目标发音风格下的录音数据时，语音转换（VC）已被证明是训练TTS系统的一种有用的数据增强技术。本文关注Lombard发音风格迁移。我们的目标是在转换说话人身份的同时，保留定义Lombard发音风格的声学属性。我们比较了具有隐式和显式声学特征条件的语音转换模型。我们观察到，我们提出的隐式条件策略获得了与基于显式声学特征条件模型相当的可懂度提升，同时也保留了说话人相似度。
英文摘要：
Text-to-Speech (TTS) systems in Lombard speaking style can improve the overall intelligibility of speech, useful for hearing loss and noisy conditions. However, training those models requires a large amount of data and the Lombard effect is challenging to record due to speaker and noise variability and tiring recording conditions. Voice conversion (VC) has been shown to be a useful augmentation technique to train TTS systems in the absence of recorded data from the target speaker in the target speaking style. In this paper, we are concerned with Lombard speaking style transfer. Our goal is to convert speaker identity while preserving the acoustic attributes that define the Lombard speaking style. We compare voice conversion models with implicit and explicit acoustic feature conditioning. We observe that our proposed implicit conditioning strategy achieves an intelligibility gain comparable to the model conditioned on explicit acoustic features, while also preserving speaker similarity.
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。
