1 citations · 1 across the 7 of their papers we have counts for
10 papers
Distributional Metrics for Evaluating Spoken Conversational Systems
Shree Harsha Bokkahalli Satish, Erica Cooper, Patrícia Schmidtová +4
Evaluating conversational systems is a difficult and unresolved problem. We introduce the Conversational Distribution Score (CDS), which compares distributions of conversational be…
A Cross Community Agenda for Speech AI
Maria Teleki, Kimi Wenzel, Anna Seo Gyeong Choi +20
Speech AI, any AI system that recognizes, transforms, or generates speech, is built and evaluated across two communities with only a small overlap: technical natural language proce…
From Seeing it to Experiencing it: Interactive Evaluation of Intersectional Voice Bias in Human-AI Speech Interaction
Shree Harsha Bokkahalli Satish, Maria Teleki, Christoph Minixhofer +3
SpeechLLMs process spoken language directly from audio, but accent and vocal identity cues can lead to biased behaviour. Current bias evaluations often miss how such bias manifests…
The Voice Behind the Words: Quantifying Intersectional Bias in SpeechLLMs
Shree Harsha Bokkahalli Satish, Christoph Minixhofer, Maria Teleki +5
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…
What Counts as Real? Speech Restoration and Voice Quality Conversion Pose New Challenges to Deepfake Detection
Shree Harsha Bokkahalli Satish, Harm Lameris, Joakim Gustafson +1
Audio anti-spoofing systems are typically trained to assign one authenticity label to an entire speech utterance. This formulation becomes under-specified for transformations where…
Lost in Phonation: Voice Quality Variation as an Evaluation Dimension for Speech Foundation Models
Harm Lameris, Shree Harsha Bokkahalli Satish, Joakim Gustafson +1
Recent advances in Speech Foundation Models (SFMs) enable direct processing of raw audio, allowing models to respond to subtle paralinguistic variation. However, how these models i…
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…