Abstract
Most methods of voice restoration for patients suffering from aphonia either produce whispered or monotone speech. Apart from intelligibility, this type of speech lacks expressiveness and naturalness due to the absence of pitch (whispered speech) or artificial generation of it (monotone speech). Existing techniques to restore prosodic information typically combine a vocoder, which parameterises the speech signal, with machine learning techniques that predict prosodic information. In contrast, this paper describes an end-to-end neural approach for estimating a fully-voiced speech waveform from whispered alaryngeal speech. By adapting our previous work in speech enhancement with generative adversarial networks, we develop a speaker-dependent model to perform whispered-to-voiced speech conversion. Preliminary qualitative results show effectiveness in re-generating voiced speech, with the creation of realistic pitch contours.
Abstract (translated)
患有失语症的患者的大多数语音恢复方法或者产生低声或单调的语音。除了可理解性之外，由于没有音调（低声说话）或人工生成（单调语音），这种类型的语音缺乏表现力和自然性。用于恢复韵律信息的现有技术通常将用于参数化语音信号的声码器与预测韵律信息的机器学习技术相结合。相比之下，本文描述了一种端到端神经方法，用于估计来自低声喉音的全浊语音波形。通过使用生成的对抗性网络调整我们以前的语音增强工作，我们开发了一种依赖于说话者的模型来执行低声到语音的语音转换。初步定性结果显示了重新生成浊音语音的有效性，并创建了逼真的音高轮廓。
URL
https://arxiv.org/abs/1808.10687
