arXiv:2606. 27536v1 Announce Type: cross Abstract: Speech emotion recognition (SER) often relies on hard consensus labels that collapse annotator disagreement.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2511. 14117v2 Announce Type: replace Abstract: Supervised classifiers output a distribution over classes but are typically trained against a single label obtained by collapsing multiple annotators into a majority vote.
arXiv:2608.28932v1 Announce Type: new Abstract: Voice products increasingly need affective cues that are present in speech but absent from transcripts. We introduce VocalAffectBench, a public, test-o...
arXiv:2609.05806v1 Announce Type: new Abstract: Emotion Recognition in Conversations (ERC) aims to identify speakers' emotions in multi-turn dialogue. Accurate emotion recognition can support a wide...
arXiv:2606. 05376v1 Announce Type: new Abstract: Many human-centered tasks, including natural language inference (NLI) and emotion recognition (ER), have multiple plausible interpretations, leading to label ambiguity and challenging disagreements across human annotators.
arXiv:2607. 18336v1 Announce Type: cross Abstract: Multimodal emotion recognition in conversation (MERC) can leverage multimodal and contextual cues to boost recognition performance.
SISER is a speaker‑invariant speech emotion recognition framework that combines wav2vec 2.0 for feature extraction with an ECAPA‑TDNN speaker discriminator in an entropy‑based adversarial training scheme. By leveraging self‑supervised representations, SISER reduces reliance on large labeled datasets and suppresses speaker identity more effectively than shallow classifiers. On the IEMOCAP benchmark, SISER achieves a UA of 60.63%, surpassing both the baseline (51.15%) and wav2vec 2.0 without speaker suppression (56.46%).