SC VALL-E: Style-Controllable Zero-Shot Text to Speech Synthesizer

TL;DR

This paper introduces SC VALL-E, a neural codec language model that enables controllable, expressive zero-shot text-to-speech synthesis by manipulating style attributes like emotion and pitch, outperforming existing models.

Contribution

The paper presents a novel style control mechanism within VALL-E, allowing for explicit manipulation of speech attributes in zero-shot synthesis, which was not possible with prior models.

Findings

SC VALL-E achieves competitive quality in expressive speech synthesis.

The model effectively controls style attributes such as emotion and pitch.

It outperforms baseline models in style control and speech quality metrics.

Abstract

Expressive speech synthesis models are trained by adding corpora with diverse speakers, various emotions, and different speaking styles to the dataset, in order to control various characteristics of speech and generate the desired voice. In this paper, we propose a style control (SC) VALL-E model based on the neural codec language model (called VALL-E), which follows the structure of the generative pretrained transformer 3 (GPT-3). The proposed SC VALL-E takes input from text sentences and prompt audio and is designed to generate controllable speech by not simply mimicking the characteristics of the prompt audio but by controlling the attributes to produce diverse voices. We identify tokens in the style embedding matrix of the newly designed style network that represent attributes such as emotion, speaking rate, pitch, and voice intensity, and design a model that can control these attributes. To evaluate the performance of SC VALL-E, we conduct comparative experiments with three representative expressive speech synthesis models: global style token (GST) Tacotron2, variational autoencoder (VAE) Tacotron2, and original VALL-E. We measure word error rate (WER), F0 voiced error (FVE), and F0 gross pitch error (F0GPE) as evaluation metrics to assess the accuracy of generated sentences. For comparing the quality of synthesized speech, we measure comparative mean option score (CMOS) and similarity mean option score (SMOS). To evaluate the style control ability of the generated speech, we observe the changes in F0 and mel-spectrogram by modifying the trained tokens. When using prompt audio that is not present in the training data, SC VALL-E generates a variety of expressive sounds and demonstrates competitive performance compared to the existing models. Our implementation, pretrained models, and audio samples are located on GitHub.