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CUEES (Correlation-gUided Encoder Selection), a lightweight heuristic that estimates complementarity through task- and category-level Pearson correlations between encoders'performance profiles, scoring a candidate set from single-encoder evaluations alone--without fusion training during selection.
Emotion preference models can be dramatically improved by addressing both data sparsity and model bias, leading to more accurate emotional assessments in multimodal contexts.
Voice recordings can reveal the oscillating states of Recurrent Respiratory Papillomatosis, providing a unique longitudinal perspective on a rare laryngeal disease.
Text-only LLMs already contain surprisingly diverse levels of auditory knowledge, and this pre-existing knowledge strongly predicts their performance when adapted for audio-language tasks.
Speech quality assessment is skewed: male listeners consistently give higher scores than female listeners, and standard MOS models learn and perpetuate this bias.
Overcome LALM's struggles with localized dialectal prosody: a new Taiwanese audio-text dataset and fine-tuning strategy boosts accuracy by 6.5% on the TAU Benchmark.
