来源简介
arXiv:2609.20081v2 Announce Type: replace Abstract: SpeechLLMs can recognise emotion by generating a label token, but this approach relies on the LLM's existing output representations. We compare this generative approach with a discriminative alternative that uses a linear classification head on top of the LLM's hidden states. Because most multimodal emotion recognition studies evaluate with ground-truth transcripts, whereas deployed systems receive ASR output, we run every comparison with both, using a compact speechLLM (frozen Whisper-medium encoder, 3B LLM) on IEMOCAP and MELD. The classification head improves Macro F1 in 10 of 14 configurations. On IEMOCAP, it improves every configuration using ASR transcripts, reaching 76.97\% Macro F1, with a significant gain when using speech alone. On MELD, where ASR transcripts have a 32.38\% word error rate, it helps with speech alone but not with ASR transcripts. The benefit thus depends on both the dataset and the transcript source: in 4 of the 6 configurations that use text, the head's effect changes sign between ground-truth and ASR transcripts. Conclusions drawn with ground-truth transcripts therefore need not hold with ASR transcripts. Finally, comparing the learned class vectors with the LLM's vocabulary shows that, on IEMOCAP and MELD, their nearest tokens are mostly emotion-related words rather than explicit emotion labels.
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采集于 2026/10/20 21:06:31（北京时间）。 arXiv 日期为本次公告时间，论文初次投稿日期请查看原文版本记录。