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Author

Alice Baird

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Conference Sep 2026

Evaluating Previous-Partner Context for Affective Influence Prediction in Dyadic Conversation

Automatic understanding of affect in conversation requires models to represent both a speaker’s behaviour and the surrounding interaction. We study whether the most recent partner segment preceding the current target segment adds predictive value in the DaiKon Influence task. Using mean-pooled Whisper-small representat...

Sharath Rao, Alice Baird, J. Clapa et al. · 0 citations
Conference Sep 2026

Cross-Cultural Generalization of Interpersonal Influence Models in Dyadic Conversation

The ACII 2026 DaiKon Influence sub-challenge evaluates segment-level prediction of affective interpersonal influence in dyadic conversation. We study whether Influence models trained on one country-defined cultural group generalize to others, restricting analysis to five countries in the current test-set result bundle:...

Rashish Tandon, Alice Baird, J. Clapa et al. · 0 citations
Conference Sep 2026

The Impact of Audio Quality Filtering on Demographic and Expressive Diversity in Dyadic Conversational Data

Audio quality filtering is a standard preprocessing step in affective speech corpus curation, yet while downstream demographic bias in speech models is well-documented, the representational consequences of filtering strategies are rarely examined. We analyse DaiKon, a multilingual dual-channel dyadic conversation corpu...

Alice Baird, Theo Lebryk, Panagiotis Tzirakis et al. · 0 citations
Preprint Aug 2026

Towards Quantifying Benchmark Optimization in ASR Models

This work presents a methodology for quantifying benchmark optimization, focusing on cases where the audio underdetermines the reference transcript, and indicates that high-performing models exhibit benchmark-conditioned behaviors that can inflate benchmark performance without reflecting improved general-purpose transc...

Theo Lebryk, David Ayllón, Alice Baird et al. · 0 citations
Jul 2026

RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems

The Real World Voice EQ Bench is introduced, a multidimensional benchmark for evaluating voice AI across text-to-speech (TTS), speech-to-speech (STS), speech understanding (SU), and automatic speech recognition (ASR), showing that performance is highly dimension-specific.

David Ayllón, Alice Baird, Jeffrey A. Brooks et al. · 2 citations

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