Search Results for author:
Found 5 papers, 2 papers with code
QS-TTS: Towards Semi-Supervised Text-to-Speech Synthesis via Vector-Quantized Self-Supervised Speech Representation Learning
This paper proposes a novel semi-supervised TTS framework, QS-TTS, to improve TTS quality with lower supervised data requirements via Vector-Quantized Self-Supervised Speech Representation Learning (VQ-S3RL) utilizing more unlabeled speech audio.
Towards High-Quality Neural TTS for Low-Resource Languages by Learning Compact Speech Representations
Moreover, we optimize the training strategy by leveraging more audio to learn MSMCRs better for low-resource languages.
A Multi-Stage Multi-Codebook VQ-VAE Approach to High-Performance Neural TTS
A vector-quantized, variational autoencoder (VQ-VAE) based feature analyzer is used to encode Mel spectrograms of speech training data by down-sampling progressively in multiple stages into MSMC Representations (MSMCRs) with different time resolutions, and quantizing them with multiple VQ codebooks, respectively.
Nana-HDR: A Non-attentive Non-autoregressive Hybrid Model for TTS
Thirdly, a duration predictor instead of an attention model that connects the above hybrid encoder and decoder.
Triple M: A Practical Text-to-speech Synthesis System With Multi-guidance Attention And Multi-band Multi-time LPCNet
In this work, a robust and efficient text-to-speech (TTS) synthesis system named Triple M is proposed for large-scale online application.
