arXiv:2606.10654v1 Announce Type: new Abstract: We investigate what self-supervised speech recognition models (S3Ms) learn about speaker groups (SGs). We examine several states of S3Ms: pretrained, finetuned on speaker identification (SID), finetuned on automatic speech recognition (ASR), and ASR-finetuned using a fairness enhancing algorithm. We find that S3Ms encode information about several speaker group categories (SGCs), including their gender, age, dialect, ethnicity, and whether they are a native speaker. We find that finetuning for SID amplifies certain SGCs, namely those whose varianc
The rapid advancement and widespread deployment of large self-supervised speech models make understanding their inherent biases and encoded information critical for ethical AI development.
This research provides insight into how foundational AI models internalize and potentially amplify sensitive speaker group information, which has profound implications for fairness, privacy, and bias in AI applications.
We gain a clearer understanding of the intrinsic societal biases embedded within leading speech AI models, enabling targeted mitigation strategies during development and deployment.
- · AI ethicists
- · Fairness enhancing algorithm developers
- · Responsible AI developers
- · Researchers in AI transparency
- · Developers ignoring ethical AI principles
- · Platforms deploying unmitigated S3Ms
- · Users affected by biased speech recognition
Self-supervised speech models are confirmed to encode and potentially amplify specific speaker group characteristics.
This understanding will drive the development and adoption of more robust fairness and privacy-preserving techniques in speech AI.
Increased transparency regarding AI biases could lead to new regulatory frameworks for AI model auditing and certification, impacting their market deployment.
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Read at arXiv cs.CL