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首页> 外文期刊>Audio, Speech, and Language Processing, IEEE Transactions on >Spectral Mapping Using Artificial Neural Networks for Voice Conversion
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Spectral Mapping Using Artificial Neural Networks for Voice Conversion

机译：使用人工神经网络进行语音映射的频谱映射

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摘要

In this paper, we use artificial neural networks (ANNs) for voice conversion and exploit the mapping abilities of an ANN model to perform mapping of spectral features of a source speaker to that of a target speaker. A comparative study of voice conversion using an ANN model and the state-of-the-art Gaussian mixture model (GMM) is conducted. The results of voice conversion, evaluated using subjective and objective measures, confirm that an ANN-based VC system performs as good as that of a GMM-based VC system, and the quality of the transformed speech is intelligible and possesses the characteristics of a target speaker. In this paper, we also address the issue of dependency of voice conversion techniques on parallel data between the source and the target speakers. While there have been efforts to use nonparallel data and speaker adaptation techniques, it is important to investigate techniques which capture speaker-specific characteristics of a target speaker, and avoid any need for source speaker's data either for training or for adaptation. In this paper, we propose a voice conversion approach using an ANN model to capture speaker-specific characteristics of a target speaker and demonstrate that such a voice conversion approach can perform monolingual as well as cross-lingual voice conversion of an arbitrary source speaker.
机译：在本文中，我们使用人工神经网络（ANN）进行语音转换，并利用ANN模型的映射功能对源说话者和目标说话者的频谱特征进行映射。使用ANN模型和最新的高斯混合模型（GMM）对语音转换进行了比较研究。使用主观和客观方法评估的语音转换结果证实，基于ANN的VC系统的性能与基于GMM的VC系统的性能相同，并且转换后的语音质量可理解且具有目标的特性。扬声器。在本文中，我们还解决了语音转换技术对源说话者和目标说话者之间的并行数据的依赖性问题。尽管已经努力使用非并行数据和说话者自适应技术，但是研究捕获目标说话者特定于说话者特征的技术，并避免对源说话者数据进行培训或自适应的任何需求，这一点很重要。在本文中，我们提出了一种使用ANN模型的语音转换方法，以捕获目标说话者的特定于说话者的特征，并证明了这种语音转换方法可以执行任意源说话者的单语和跨语语音转换。

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