Proposing Two Data Augmentation Techniques for ASR with Limited Data: Gradual Masking and Word Frequency-Aware Masking
Subject Areas : مهندسی برق و کامپیوتر
Ma. Asadolahzade Kermanshahi
1
,
A. Akbari Azirani
2
*
,
B. Nasersharif
3
1 - School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
2 - School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
3 - Department of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
Keywords: Speech recognition, word masking, data augmentation, limited data.,
Abstract :
Data scarcity is the main challenge for DNN-based speech recognition, and data augmentation serves as an effective solution. This paper presents a comprehensive taxonomy of data augmentation methods in speech recognition while investigating the effectiveness of the most important techniques in this domain, masking-based methods, under limited data conditions. The examined methods include two powerful approaches: SpecAugment and word masking. Despite their proven effectiveness in high-resource scenarios, these methods have been less studied under limited data conditions. After analyzing the shortcomings of word masking in limited data settings, we propose two novel methods: (1) Gradual masking, which begins training with frame-level masking and then transitions to word-level masking; and (2) Word frequency-aware masking, which masks high-frequency words first, followed by low-frequency words. Experiments on the 100-hour LibriSpeech subset demonstrate that our first proposed method achieves a WER of 6.8% on the clean test set and 18.2% on the challenging test set, representing improvements of 6.8% and 4.2% respectively over SpecAugment. The second proposed method reaches a WER of 6.6% on the clean test set and 17.3% on the challenging test set, achieving improvements of 9.6% and 8.9% respectively compared to SpecAugment.
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