The rapid development of SpeechLLM-based conversational AI systems has created a need for robustly benchmarking these efforts, including aspects of fairness and bias. At present, such benchmarks typically rely on multiple choice question answering (MCQA). In this paper, we present the first token-level probabilistic evaluation and response-based study of several issues affecting the use of MCQA in SpeechLLM benchmarking: 1) we examine how model temperature and prompt design affect gender and positional bias on an MCQA gender-bias benchmark; 2) we examine how these biases are affected by the gender of the input voice; and 3) we study to what extent observed trends carry over to a second gender-bias benchmark. Our results show that concerns about positional bias from the text domain are equally valid in the speech domain. We also find the effect to be stronger for female voices than for male voices. To our knowledge, this is the first study to isolate positional bias effects in SpeechLLM-based gender-bias benchmarks. We conclude that current MCQA benchmarks do not account for speech-based bias and alternative strategies are needed to ensure fairness towards all users.

A new wave of speech foundation models is emerging, capable of processing spoken language directly from audio. These models promise more expressive and emotionally aware interactions by retaining prosodic information throughout conversations. 'Hear Me Out' evaluates their ability to preserve crucial vocal cues, enabling users to explore how variations in speaker characteristics and paralinguistic features influence AI responses. Through real-time voice conversion, users can ask a question and then re-ask it with a modified one, immediately observing differences in response tone, phrasing, and behavior. The system presents paired responses side by side, offering direct comparisons of AI interpretations of both the original and transformed voices, thereby highlighting potential biases. By creating inquiry into speaker modeling, contextual understanding, and fairness, this immersive experience encourages users to reflect on identity, voice, and also promote inclusive future research.