Tacotron: Towards End-to-End Speech Synthesis

TL;DR

Tacotron is an end-to-end neural speech synthesis system that directly converts characters to speech, achieving high naturalness and faster synthesis compared to traditional methods.

Contribution

It introduces a fully trainable, sequence-to-sequence model for speech synthesis that simplifies the pipeline and improves naturalness and speed.

Findings

Achieves a 3.82 MOS score, outperforming traditional parametric systems.

Generates speech faster than autoregressive models.

Can be trained from scratch with minimal domain expertise.

Abstract

A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module. Building these components often requires extensive domain expertise and may contain brittle design choices. In this paper, we present Tacotron, an end-to-end generative text-to-speech model that synthesizes speech directly from characters. Given <text, audio> pairs, the model can be trained completely from scratch with random initialization. We present several key techniques to make the sequence-to-sequence framework perform well for this challenging task. Tacotron achieves a 3.82 subjective 5-scale mean opinion score on US English, outperforming a production parametric system in terms of naturalness. In addition, since Tacotron generates speech at the frame level, it's substantially faster than sample-level autoregressive methods.