EPIC TTS Models: Empirical Pruning Investigations Characterizing Text-To-Speech Models. (arXiv:2209.10890v1 [eess.AS])

Neural models are known to be over-parameterized, and recent work has shown
that sparse text-to-speech (TTS) models can outperform dense models. Although a
plethora of sparse methods has been proposed for other domains, such methods
have rarely been applied in TTS. In this work, we seek to answer the question:
what are the characteristics of selected sparse techniques on the performance
and model complexity? We compare a Tacotron2 baseline and the results of
applying five techniques. We then evaluate the …

arxiv investigations pruning speech text text-to-speech tts