Speech-to-text alignment is a critical component of neural textto-speech
(TTS) models. Autoregressive TTS models typically use an attention mechanism to
learn these alignments on-line. However, these alignments tend to be brittle
and often fail to generalize to long utterances and out-of-domain text, leading
to missing or repeating words. Most non-autoregressive endto-end TTS models
rely on durations extracted from external sources. In this paper we leverage
the alignment mechanism proposed in RAD-TTS as a generic alignment learning
framework, easily applicable to a variety of neural TTS models. The framework
combines forward-sum algorithm, the Viterbi algorithm, and a simple and
efficient static prior. In our experiments, the alignment learning framework
improves all tested TTS architectures, both autoregressive (Flowtron, Tacotron
2) and non-autoregressive (FastPitch, FastSpeech 2, RAD-TTS). Specifically, it
improves alignment convergence speed of existing attention-based mechanisms,
simplifies the training pipeline, and makes the models more robust to errors on
long utterances. Most importantly, the framework improves the perceived speech
synthesis quality, as judged by human evaluators.

该研究提出了一种基于 RAD-TTS 的对齐机制作为 TTS 模型的通用对齐学习框架，该框架结合了前向和算法、维特比算法和简单有效的静态先验，可提高 TTS 模型的对齐收敛速度，并使模型对长语音的错误更加鲁棒，从而提高口头合成的质量。