3 accepted papers
Previous works in zero-shot text-to-speech (ZS-TTS) have attempted to enhance its systems by enlarging the training data through crowd-sourcing or augmenting existing speech data. However, the use of low-quality data has led to a decline in the overall system performance. To avoid such degradation,…
This paper presents HierSpeech, a high-quality end-to-end text-to-speech (TTS) system based on a hierarchical conditional variational autoencoder (VAE) utilizing self-supervised speech representations. Recently, single-stage TTS systems, which directly generate raw speech waveform from text, have be…
Adaptive text-to-speech (TTS) has attracted increasing interests for the purpose of training TTS systems without tons of high quality data. Nevertheless, existing adaptive TTS systems still show low adaptation quality for novel speakers, since it is hard to learn an extensive speaking style with lim…