Investigation of Data Augmentation Techniques for Disordered Speech Recognition
arXiv:2201.05562 · doi:10.21437/Interspeech.2020-1161
Abstract
Disordered speech recognition is a highly challenging task. The underlying neuro-motor conditions of people with speech disorders, often compounded with co-occurring physical disabilities, lead to the difficulty in collecting large quantities of speech required for system development. This paper investigates a set of data augmentation techniques for disordered speech recognition, including vocal tract length perturbation (VTLP), tempo perturbation and speed perturbation. Both normal and disordered speech were exploited in the augmentation process. Variability among impaired speakers in both the original and augmented data was modeled using learning hidden unit contributions (LHUC) based speaker adaptive training. The final speaker adapted system constructed using the UASpeech corpus and the best augmentation approach based on speed perturbation produced up to 2.92% absolute (9.3% relative) word error rate (WER) reduction over the baseline system without data augmentation, and gave an overall WER of 26.37% on the test set containing 16 dysarthric speakers.
arXiv commentProceedings of INTERSPEECH 2020
References in corpus (5)
- SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
- Deep Scattering Spectrum
- The Microsoft 2017 Conversational Speech Recognition System
- Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation
- English Broadcast News Speech Recognition by Humans and Machines
Cited by in corpus (7)
- Recent Progress in the CUHK Dysarthric Speech Recognition System
- Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition
- Spectro-Temporal Deep Features for Disordered Speech Assessment and Recognition
- Variational Auto-Encoder Based Variability Encoding for Dysarthric Speech Recognition
- Overview of Automatic Speech Analysis and Technologies for Neurodegenerative Disorders: Diagnosis and Assistive Applications
- Significance of Data Augmentation for Improving Cleft Lip and Palate Speech Recognition
- Adversarial Data Augmentation for Disordered Speech Recognition