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pro vyhledávání: '"Kong, Jungil"'
Autor:
Kong, Jungil, Lee, Junmo, Kim, Jeongmin, Kim, Beomjeong, Park, Jihoon, Kong, Dohee, Lee, Changheon, Kim, Sangjin
In this work, we propose a novel method for modeling numerous speakers, which enables expressing the overall characteristics of speakers in detail like a trained multi-speaker model without additional training on the target speaker's dataset. Althoug
Externí odkaz:
http://arxiv.org/abs/2311.11745
Single-stage text-to-speech models have been actively studied recently, and their results have outperformed two-stage pipeline systems. Although the previous single-stage model has made great progress, there is room for improvement in terms of its in
Externí odkaz:
http://arxiv.org/abs/2307.16430
Several recent end-to-end text-to-speech (TTS) models enabling single-stage training and parallel sampling have been proposed, but their sample quality does not match that of two-stage TTS systems. In this work, we present a parallel end-to-end TTS m
Externí odkaz:
http://arxiv.org/abs/2106.06103
Several recent work on speech synthesis have employed generative adversarial networks (GANs) to produce raw waveforms. Although such methods improve the sampling efficiency and memory usage, their sample quality has not yet reached that of autoregres
Externí odkaz:
http://arxiv.org/abs/2010.05646
Recently, text-to-speech (TTS) models such as FastSpeech and ParaNet have been proposed to generate mel-spectrograms from text in parallel. Despite the advantage, the parallel TTS models cannot be trained without guidance from autoregressive TTS mode
Externí odkaz:
http://arxiv.org/abs/2005.11129