The Voice Behind the Words: Quantifying Intersectional Bias in SpeechLLMs
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 comparisons, and Best-Worst Scaling with human validation, we detect consistent disparities. Eastern European-accented speech receives lower helpfulness scores, particularly for female-presenting voices. The bias is implicit: responses remain polite but differ in helpfulness. While LLM judges capture the directional trend of these biases, human evaluators exhibit significantly higher sensitivity, uncovering sharper intersectional disparities.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper presents a comprehensive analysis of intersectional bias in SpeechLLMs, revealing significant disparities in response quality based on accent and gender. The innovative methodology and rigorous experimental design contribute valuable insights into the challenges of bias in AI, emphasizing the need for sensitive evaluation methods in the field.
Methodology
The methodology employed in this study is robust and innovative, utilizing a large-scale dataset of 2,880 interactions to evaluate intersectional bias in SpeechLLMs. The use of voice cloning to maintain linguistic content while varying accent and gender is a significant strength, allowing for a controlled analysis of bias. The combination of pointwise ratings, pairwise comparisons, and Best-Worst Scaling (BWS) provides a comprehensive approach to measuring bias, although the reliance on automated LLM judges alongside human evaluations raises questions about the sensitivity of the automated methods.
Results & evaluation
The experiments are well-structured, with clear preliminary checks to ensure the validity of the SpeechLLMs' outputs. The findings reveal significant biases, particularly against Eastern European-accented female voices, which are supported by both automated and human evaluations. However, the statistical significance of some results could be further clarified, especially in the context of the varying performance across different models.
Reproducibility
The authors have made efforts to ensure reproducibility by releasing their dataset and evaluation prompts. However, the paper could benefit from more detailed descriptions of the experimental setup, including the specific configurations of the SpeechLLMs used, to facilitate replication by other researchers.
Limitations
One limitation is the potential for bias in the human evaluators themselves, which could affect the results. Additionally, the study's focus on only three SpeechLLMs may limit the generalizability of the findings to other models. The reliance on synthetic speech may also introduce artifacts that do not reflect real-world interactions.
Broader impact
This research has significant implications for the development of SpeechLLMs and their deployment in real-world applications. By highlighting the intersectional biases present in these models, the study underscores the importance of addressing such biases to ensure equitable AI systems. The findings could inform future research and development practices, leading to more inclusive and fair AI technologies. This paper presents a comprehensive analysis of intersectional bias in SpeechLLMs, revealing significant disparities in response quality based on accent and gender. The innovative methodology and rigorous experimental design contribute valuable insights into the challenges of bias in AI, emphasizing the need for sensitive evaluation methods in the field.
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