arXiv:2603.16941v2 Announce Type: replace-cross Abstract: Speech Large Language Models (SpeechLLMs) process spoken input directly, retaining cues such as accent and perceived gender that were previously removed in cascaded pipelines. This introduces speaker identity dependent variation in responses. We present a large-scale intersectional evaluation of accent and gender bias in three SpeechLLMs using 2,880 controlled interactions across six English accents and two gender presentations, keeping linguistic content constant through voice cloning. Using pointwise LLM-judge ratings, pairwise compar
The proliferation of SpeechLLMs capable of directly processing spoken input necessitates immediate and thorough bias assessment to ensure equitable and reliable AI development.
Understanding and mitigating intersectional biases in SpeechLLMs is crucial for preventing the perpetuation and amplification of societal inequalities through AI applications, impacting trust and adoption.
The direct processing of spoken input by SpeechLLMs now introduces identity-dependent variations, requiring new methodologies and considerations for bias detection beyond traditional text-based systems.
- · AI ethics researchers
- · Developers of bias detection tools
- · Users advocating for equitable AI
- · Developers of unmitigated SpeechLLMs
- · Organizations deploying biased AI systems
- · Groups negatively impacted by biased AI
Increased scrutiny and standardization for bias evaluation in SpeechLLMs will become an industry norm.
Demand for 'de-biased' or 'bias-aware' SpeechLLM products will grow, creating a competitive advantage for early adopters.
Regulatory bodies may introduce specific guidelines or legislation regarding bias testing and disclosure for SpeechLLMs, influencing product development cycles.
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