Enhancing Crowdsourced Audio for Text-to-Speech Models

TL;DR

This paper introduces a denoising and filtering pipeline for crowdsourced audio data, improving its quality for training text-to-speech models, demonstrated on Catalan Commonvoice data with significant quality gains.

Contribution

The paper presents a novel denoising and automatic filtering approach using NISQA models to enhance crowdsourced audio for TTS training, especially for low-resource languages.

Findings

UTMOS Score increased by 0.4 after enhancement

Significant quality improvement in crowdsourced Catalan audio

Effective filtering retains high-quality samples for TTS training

Abstract

High-quality audio data is a critical prerequisite for training robust text-to-speech models, which often limits the use of opportunistic or crowdsourced datasets. This paper presents an approach to overcome this limitation by implementing a denoising pipeline on the Catalan subset of Commonvoice, a crowd-sourced corpus known for its inherent noise and variability. The pipeline incorporates an audio enhancement phase followed by a selective filtering strategy. We developed an automatic filtering mechanism leveraging Non-Intrusive Speech Quality Assessment (NISQA) models to identify and retain the highest quality samples post-enhancement. To evaluate the efficacy of this approach, we trained a state of the art diffusion-based TTS model on the processed dataset. The results show a significant improvement, with an increase of 0.4 in the UTMOS Score compared to the baseline dataset without enhancement. This methodology shows promise for expanding the utility of crowdsourced data in TTS applications, particularly for mid to low resource languages like Catalan.