Evaluating Bias in Phoneme-Based Automatic Speech Recognition Systems: An Analysis of IPA Transcription Models
arXiv:2606.11639v1 Announce Type: new Abstract: The popularization of automatic speech recognition (ASR) systems has increased exploration of the demographic biases related to race, age, gender, and accent, often formed from imbalanced training data. Most of these studies focused on standard grapheme-based ASR systems with comparatively little emphasis on phoneme-based systems, such as models that produce International Phonetic Alphabet (IPA) representations. As ASR systems shift toward multilingual support and low-resource language modeling, IPA-based layers serve as a critical, language-agno
The increasing focus on multilingual support and low-resource language modeling in ASR systems highlights the critical role of phoneme-based analysis and its inherent biases.
Bias in phoneme-based ASR systems can perpetuate inequality and limit access for diverse populations, impacting global communication and AI adoption at scale.
The understanding and mitigation of demographic biases in foundational AI models, particularly in speech recognition, will become a more explicit development priority.
- · AI ethicists
- · Multilingual AI developers
- · Users of diverse dialects/accents
- · Research institutions
- · ASR systems with unaddressed biases
- · Companies neglecting bias mitigation
- · Monolingual AI approaches
Improved fairness and accuracy in ASR systems, especially for non-dominant language groups.
Increased demand for diverse training datasets and more inclusive AI development practices globally.
Reduced digital divides and greater cultural inclusivity as AI technologies become more accessible and equitable across demographics.
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