对比多种预训练模型，发现专注语音副语言特征的模型更适合跨语料库情感识别。
Rethinking Cross-Corpus Speech Emotion Recognition Benchmarking: Are Paralinguistic Pre-Trained Representations Sufficient?
- 用多类预训练模型对比，重点评估语音副语言特征模型表现
- TRILLsson模型在跨语料库情感识别中准确率领先其他模型
- 研究提醒应重视副语言特征模型，提升评测可靠性
近期用于评估跨语料库语音情感识别（SER）的预训练模型（PTM）基准忽略了专为语音副语言处理（PSP）设计的模型，引发可信度质疑，因为情感识别本质上是副语言任务。本文假设专注于PSP的PTM在跨语料库SER中表现更优。为此，我们分析了包括副语言、单语、多语及说话人识别在内的多种SOTA PTM表示。结果证实，TRILLsson（一种副语言PTM）性能优于其他模型，强化了在跨语料库SER基准中纳入PSP聚焦模型的必要性。本研究提升了基准可信度，并为可靠跨语料库SER的PTM评估提供指导。
原文摘要 · Abstract (English)
Recent benchmarks evaluating pre-trained models (PTMs) for cross-corpus speech emotion recognition (SER) have overlooked PTM pre-trained for paralinguistic speech processing (PSP), raising concerns about their reliability, since SER is inherently a paralinguistic task. We hypothesize that PSP-focused PTM will perform better in cross-corpus SER settings. To test this, we analyze state-of-the-art PTMs representations including paralinguistic, monolingual, multilingual, and speaker recognition. Our results confirm that TRILLsson (a paralinguistic PTM) outperforms others, reinforcing the need to consider PSP-focused PTMs in cross-corpus SER benchmarks. This study enhances benchmark trustworthiness and guides PTMs evaluations for reliable cross-corpus SER.
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