Text Generation with Speech Synthesis for ASR Data Augmentation
Zhuangqun Huang Gil Keren Ziran Jiang Shashank Jain David Goss-Grubbs Nelson Cheng Farnaz Abtahi Duc Le David Zhang Antony D'Avirro Ethan Campbell-Taylor Jessie Salas Irina-Elena Veliche Xi Chen
Aiming at reducing the reliance on expensive human annotations, data
synthesis for Automatic Speech Recognition (ASR) has remained an active area of
research. While prior work mainly focuses on synthetic speech generation for
ASR data augmentation, its combination with text generation methods is
considerably less explored. In this work, we explore text augmentation for ASR
using large-scale pre-trained neural networks, and systematically compare those
to traditional text augmentation methods. The generated synthetic texts are
then converted to synthetic speech using a text-to-speech (TTS) system and
added to the ASR training data. In experiments conducted on three datasets, we
find that neural models achieve 9%-15% relative WER improvement and outperform
traditional methods. We conclude that text augmentation, particularly through
modern neural approaches, is a viable tool for improving the accuracy of ASR
systems.