6 accepted papers
Speech Large Language Models (SLLMs) enable high-level emotion reasoning, but often produce ungrounded, text-biased judgments without verifiable acoustic evidence. In contrast, SSL encoders such as WavLM yield strong acoustic representations yet remain opaque discriminative models that offer limited…
Speech Emotion Recognition (SER) is typically trained and evaluated on majority-voted labels, which simplifies benchmarking but masks subjectivity and provides little transparency into why predictions are made. This neglects valid minority annotations and limits interpretability. We propose an expla…
Speech signal analysis poses significant challenges, particularly in tasks such as speech quality evaluation and profiling, where the goal is to predict multiple perceptual and objective metrics. For instance, metrics like PESQ (Perceptual Evaluation of Speech Quality), STOI (Short-Time Objective In…
Accurate assessment of social reciprocity is crucial for early diagnosis and intervention in Autism Spectrum Disorder (ASD). Traditional methods, often relying on unimodal data or lacking in cross-modal alignment, do not fully capture the complexity of social reciprocity. To address these limitation…
In generalized Speech Emotion Recognition (SER), traditional generalization techniques like transfer learning and domain adaptation rely on access to some amount of unlabeled target domain data. However, with increasing privacy concerns, building SER systems under zero-shot scenarios, where no targe…
Modeling cross-lingual speech emotion recognition (SER) has become more prevalent because of its diverse applications. Existing studies have mostly focused on technical approaches that adapt the feature, domain, or label across languages, without considering in detail the similarities between the la…