  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.
