The use of normalizing flow has been introduced to address output diversity issues in parallel
TTS architectures. This technique is utilized to model the duration of speech, as evidenced by
studies conducted in [250, 377, 493]. One such flow-based generative model is Glow-TTS [250],
developed specifically for parallel TTS without the need for an external aligner. The model employs
the generic Glow architecture previously used in computer vision and vocoder models to produce
mel-spectrograms from text inputs, which are then converted to speech audio. Glow-TTS has
demonstrated superior synthesis speed over the autoregressive model, Tacotron 2, while maintaining
comparable speech quality.
AReviewofDeepLearningTechniquesforSpeechProcessing