1 citations · 1 across the 3 of their papers we have counts for
4 papers
When Voice Matters: Evidence of Gender Disparity in Positional Bias of SpeechLLMs
Shree Harsha Bokkahalli Satish, Gustav Eje Henter, Éva Székely
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, s…
Do Bias Benchmarks Generalise? Evidence from Voice-based Evaluation of Gender Bias in SpeechLLMs
Shree Harsha Bokkahalli Satish, Gustav Eje Henter, Éva Székely
Recent work in benchmarking bias and fairness in speech large language models (SpeechLLMs) has relied heavily on multiple-choice question answering (MCQA) formats. The model is tas…
Who Gets the Mic? Investigating Gender Bias in the Speaker Assignment of a Speech-LLM
Dariia Puhach, Amir H. Payberah, Éva Székely
Similar to text-based Large Language Models (LLMs), Speech-LLMs exhibit emergent abilities and context awareness. However, whether these similarities extend to gender bias remains…
Will AI shape the way we speak? The emerging sociolinguistic influence of synthetic voices
Éva Székely, Jūra Miniota, Míša +1
The growing prevalence of conversational voice interfaces, powered by developments in both speech and language technologies, raises important questions about their influence on hum…