论文标题
通过F0轨迹的功能数据分析改善扬声器去识别
Improving speaker de-identification with functional data analysis of f0 trajectories
论文作者
论文摘要
由于存储在不同类型的数据库中的语音数据不断增加，因此语音隐私已成为主要问题。为了应对这种关注，演讲研究人员开发了各种说话者去识别的方法。最先进的解决方案利用了深度学习解决方案，这些解决方案可能有效，但可能不可用或不切实际地申请，例如资源不足的语言。代式修改是一种更简单但有效的扬声器识别方法，不需要培训数据。尽管如此，在共振剂匿名语音中剩余的原则模式可能包含依赖说话者的线索。这项研究介绍了一种新颖的扬声器去识别方法，除了简单的强剂移动外，该方法还基于功能数据分析来操纵F0轨迹。拟议的扬声器去识别方法将以语音可控的方式隐藏合理地识别音高特征，并改善基于赋形的扬声器的扬声器，将其取消识别高达25％。
Due to a constantly increasing amount of speech data that is stored in different types of databases, voice privacy has become a major concern. To respond to such concern, speech researchers have developed various methods for speaker de-identification. The state-of-the-art solutions utilize deep learning solutions which can be effective but might be unavailable or impractical to apply for, for example, under-resourced languages. Formant modification is a simpler, yet effective method for speaker de-identification which requires no training data. Still, remaining intonational patterns in formant-anonymized speech may contain speaker-dependent cues. This study introduces a novel speaker de-identification method, which, in addition to simple formant shifts, manipulates f0 trajectories based on functional data analysis. The proposed speaker de-identification method will conceal plausibly identifying pitch characteristics in a phonetically controllable manner and improve formant-based speaker de-identification up to 25%.
