83 papers with code • 5 benchmarks • 16 datasets
Text-To-Speech Synthesis is a machine learning task that involves converting written text into spoken words. The goal is to generate synthetic speech that sounds natural and resembles human speech as closely as possible.
In this paper, we propose FastSpeech 2, which addresses the issues in FastSpeech and better solves the one-to-many mapping problem in TTS by 1) directly training the model with ground-truth target instead of the simplified output from teacher, and 2) introducing more variation information of speech (e. g., pitch, energy and more accurate duration) as conditional inputs.
Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention
This paper describes a novel text-to-speech (TTS) technique based on deep convolutional neural networks (CNN), without use of any recurrent units.
Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram
We propose Parallel WaveGAN, a distillation-free, fast, and small-footprint waveform generation method using a generative adversarial network.
In this work, we propose "global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system.
Compared with traditional concatenative and statistical parametric approaches, neural network based end-to-end models suffer from slow inference speed, and the synthesized speech is usually not robust (i. e., some words are skipped or repeated) and lack of controllability (voice speed or prosody control).