Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End Speech Recognition Systems

• URL: http://arxiv.org/abs/2307.02009v1
• Date: Wed, 5 Jul 2023 03:39:40 GMT
• Title: Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End Speech Recognition Systems
• Authors: Tanvina Patel and Odette Scharenborg
• Abstract summary: We aim to reduce bias against different age groups and non-native speakers of Dutch. For an end-to-end (E2E) ASR system, we use state-of-the-art speed perturbation and spectral augmentation as data augmentation techniques. The combination of data augmentation and VTLN reduced the average WER and bias across various speaker groups by 6.9% and 3.9%, respectively.
• Score: 17.75067255600971
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech technology has improved greatly for norm speakers, i.e., adult native speakers of a language without speech impediments or strong accents. However, non-norm or diverse speaker groups show a distinct performance gap with norm speakers, which we refer to as bias. In this work, we aim to reduce bias against different age groups and non-native speakers of Dutch. For an end-to-end (E2E) ASR system, we use state-of-the-art speed perturbation and spectral augmentation as data augmentation techniques and explore Vocal Tract Length Normalization (VTLN) to normalise for spectral differences due to differences in anatomy. The combination of data augmentation and VTLN reduced the average WER and bias across various diverse speaker groups by 6.9% and 3.9%, respectively. The VTLN model trained on Dutch was also effective in improving performance of Mandarin Chinese child speech, thus, showing generalisability across languages

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