VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis

  title={VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis},
  author={Hui-Ling Lu and Zhiyong Wu and Xixin Wu and Xu Li and Shiyin Kang and Xunying Liu and Helen M. Meng},
  • Hui-Ling Lu, Zhiyong Wu, +4 authors H. Meng
  • Published 2021
  • Computer Science, Engineering
  • ArXiv
This paper describes a variational auto-encoder based nonautoregressive text-to-speech (VAENAR-TTS) model. The autoregressive TTS (AR-TTS) models based on the sequenceto-sequence architecture can generate high-quality speech, but their sequential decoding process can be time-consuming. Recently, non-autoregressive TTS (NAR-TTS) models have been shown to be more efficient with the parallel decoding process. However, these NAR-TTS models rely on phoneme-level durations to generate a hard… Expand

Figures from this paper

PAMA-TTS: Progression-Aware Monotonic Attention for Stable Seq2Seq TTS With Accurate Phoneme Duration Control
  • Yunchao He, Jian Luan, Yujun Wang
  • Computer Science
  • 2021
Experimental results prove that PAMA-TTS achieves the highest naturalness, while has on-par or even better duration controllability than the duration-informed model. Expand


Flow-TTS: A Non-Autoregressive Network for Text to Speech Based on Flow
Experiments on LJSpeech show that the speech quality of Flow-TTS heavily approaches that of human and is even better than that of autoregressive model Tacotron 2. Expand
FastSpeech: Fast, Robust and Controllable Text to Speech
A novel feed-forward network based on Transformer to generate mel-spectrogram in parallel for TTS is proposed, which speeds up mel-Spectrogram generation by 270x and the end-to-end speech synthesis by 38x and is called FastSpeech. Expand
Tacotron: Towards End-to-End Speech Synthesis
Tacotron is presented, an end-to-end generative text- to-speech model that synthesizes speech directly from characters that achieves a 3.82 subjective 5-scale mean opinion score on US English, outperforming a production parametric system in terms of naturalness. Expand
Neural Speech Synthesis with Transformer Network
This paper introduces and adapt the multi-head attention mechanism to replace the RNN structures and also the original attention mechanism in Tacotron2, and achieves state-of-the-art performance and close to human quality. Expand
Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions
This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text. The system is composed of a recurrent sequence-to-sequence feature prediction network that mapsExpand
Attention-Based Models for Speech Recognition
The attention-mechanism is extended with features needed for speech recognition and a novel and generic method of adding location-awareness to the attention mechanism is proposed to alleviate the issue of high phoneme error rate. Expand
Statistical parametric speech synthesis using deep neural networks
This paper examines an alternative scheme that is based on a deep neural network (DNN), the relationship between input texts and their acoustic realizations is modeled by a DNN, and experimental results show that the DNN- based systems outperformed the HMM-based systems with similar numbers of parameters. Expand
Deep Voice 3: 2000-Speaker Neural Text-to-Speech
Deep Voice 3 is presented, a fully-convolutional attention-based neural text-to-speech (TTS) system that matches state-of-the-art neural speech synthesis systems in naturalness while training ten times faster. Expand
FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow
This paper turns to generative flow, an elegant technique to model complex distributions using neural networks, and design several layers of flow tailored for modeling the conditional density of sequential latent variables, achieving comparable performance with state-of-the-art non-autoregressive NMT models. Expand
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
It is demonstrated that modeling periodic patterns of an audio is crucial for enhancing sample quality and the generality of HiFi-GAN is shown to the mel-spectrogram inversion of unseen speakers and end-to-end speech synthesis. Expand