Text-To-Speech Data Augmentation for Low Resource Speech Recognition

Understand

Nowadays, the main problem of deep learning techniques used in the development of automatic speech recognition (ASR) models is the lack of transcribed data.

• The goal of this research is to propose a new data augmentation method to improve ASR models for agglutinative and low-resource languages.
• This novel data augmentation method generates both synthetic text and synthetic audio.
• Some experiments were conducted using the corpus of the Quechua language, which is an agglutinative and low-resource language.