标题： 基于隐式和显式声学特征条件的伦巴第语说话风格语音转换 显示英文标题

标题： Voice Conversion for Lombard Speaking Style with Implicit and Explicit Acoustic Feature Conditioning

摘要： 文本到语音（TTS）系统采用伦巴第语风格可以提高语音的整体可懂性，这对听力损失和嘈杂环境很有用。 然而，训练这些模型需要大量数据，由于说话人和噪声的变异性以及令人疲惫的录音条件，伦巴第效应难以记录。 语音转换（VC）已被证明是在没有目标说话人目标语种录音数据的情况下训练TTS系统的有用增强技术。 在本文中，我们关注伦巴第语风格转换。 我们的目标是在转换说话人身份的同时保留定义伦巴第语风格的声学特征。 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在可懂性方面取得的增益与基于显式声学特征的模型相当，同时还能保持说话人相似性。 显示英文摘要

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.