2 citations · 3 across the 4 of their papers we have counts for
5 papers
Towards High-Quality Neural TTS for Low-Resource Languages by Learning Compact Speech Representations
Haohan Guo, Fenglong Xie, Xixin Wu +2
This paper aims to enhance low-resource TTS by reducing training data requirements using compact speech representations. A Multi-Stage Multi-Codebook (MSMC) VQ-GAN is trained to le…
A Multi-Stage Multi-Codebook VQ-VAE Approach to High-Performance Neural TTS
Haohan Guo, Fenglong Xie, Frank K. Soong +2
We propose a Multi-Stage, Multi-Codebook (MSMC) approach to high-performance neural TTS synthesis. A vector-quantized, variational autoencoder (VQ-VAE) based feature analyzer is us…
Nana-HDR: A Non-attentive Non-autoregressive Hybrid Model for TTS
Shilun Lin, Wenchao Su, Li Meng +3
This paper presents Nana-HDR, a new non-attentive non-autoregressive model with hybrid Transformer-based Dense-fuse encoder and RNN-based decoder for TTS. It mainly consists of thr…
Triple M: A Practical Text-to-speech Synthesis System With Multi-guidance Attention And Multi-band Multi-time LPCNet
Shilun Lin, Fenglong Xie, Li Meng +2
In this work, a robust and efficient text-to-speech (TTS) synthesis system named Triple M is proposed for large-scale online application. The key components of Triple M are: 1) A s…
LP-WaveNet: Linear Prediction-based WaveNet Speech Synthesis
Min-Jae Hwang, Frank Soong, Eunwoo Song +3
We propose a linear prediction (LP)-based waveform generation method via WaveNet vocoding framework. A WaveNet-based neural vocoder has significantly improved the quality of parame…