Enhancement of a Multilingual Model for Automatic Speech Recognition Analysis of Time and Frequency Domain Data Augmentation

<p>In recent years, significant progress has been made in automatic speech recognition (ASR) systems, especially for languages with abundant transcribed speech data. However, freely accessible models for low-resource languages, particularly those other than English, are still scarce. This study aims to enhance the performance of the prominent multilingual ASR model, Whisper, through fine-tuning and data augmentation techniques. Comprehensive and systematic experiments are conducted on time, frequency and time-frequency domain augmentation strategies across multiple low-resource languages (German, Farsi, Arabic, Russian) to investigate whether these techniques could improve ASR performance in low-resource settings.</p>