Spontaneous speech has many affective and pragmatic functions that are interesting and challenging to model in TTS. However, the presence of reduced articulation, fillers, repetitions, and other disfluencies in spontaneous speech make the text and acoustics less aligned than in read speech, which is problematic for attention-based TTS. We propose a TTS architecture that can rapidly learn to speak from small and irregular datasets, while also reproducing the diversity of expressive phenomena present in spontaneous speech. Specifically, we add utterance-level prosody control to an existing neural HMM-based TTS system which is capable of stable, monotonic alignments for spontaneous speech. We objectively evaluate control accuracy and perform perceptual tests that demonstrate that prosody control does not degrade synthesis quality. To exemplify the power of combining prosody control and ecologically valid data for reproducing intricate spontaneous speech phenomena, we evaluate the system's capability of synthesizing two types of creaky voice. Audio samples are available at https://www.speech.kth.se/tts-demos/prosodic-hmm/

翻译：自发语音具有丰富的情感与语用功能，在文本转语音建模中兼具趣味性与挑战性。然而，自发语音中的弱发音、填充词、重复及其他不流畅现象，导致文本与声学特征的对应关系弱于朗读语音，这对基于注意力机制的文本转语音系统构成困难。我们提出一种能够快速从小型非规则数据集中学习语音生成的文本转语音架构，同时可复现自发语音中多样的表达现象。具体而言，我们为现有基于神经隐马尔可夫模型的文本转语音系统增加句子级韵律控制能力，该系统可实现自发语音的稳定单调对齐。我们通过客观评估控制精度并开展感知测试，证明韵律控制不会降低合成质量。为展示结合韵律控制与生态有效数据在复现复杂自发语音现象方面的潜力，我们评估系统合成两种类型气声的能力。音频样本见 https://www.speech.kth.se/tts-demos/prosodic-hmm/