使用领域知识声学特征对乌尔都语语音情感识别的跨语料库验证
Cross-Corpus Validation of Speech Emotion Recognition in Urdu using Domain-Knowledge Acoustic Features
AI总结：
本研究在跨语料库设置下调查了乌尔都语语音情感识别，使用eGeMAPS和ComParE声学特征及逻辑回归和多层感知机分类器，证明跨语料库评估比同语料库验证更能真实反映模型鲁棒性。
摘要：
语音情感识别（SER）是一项关键的情感计算技术，能够实现具备情感智能的人工智能。尽管 SER 总体上具有挑战性，但对于乌尔都语等低资源语言而言尤为困难。本研究在跨语料库设置下调查了乌尔都语 SER，这是一个在很大程度上未被探索的领域。我们采用跨语料库评估框架，在三个不同的乌尔都语情感语音数据集上测试模型的泛化能力。使用两个标准的基于领域知识的声学特征集 eGeMAPS 和 ComParE 将语音信号表示为特征向量，随后将其传递给逻辑回归和多层感知机分类器。在考虑类别标签不平衡的同时，使用未加权平均召回率（UAR）评估分类性能。结果表明，同语料库验证通常会高估性能，UAR 超出跨语料库验证高达 100%，这凸显了跨语料库评估为模型鲁棒性提供了更现实的衡量标准。总体而言，这项工作强调了对乌尔都语 SER 进行跨语料库验证的重要性，其影响有助于推动针对代表性不足语言社区的情感计算研究。
英文摘要：
Speech Emotion Recognition (SER) is a key affective computing technology that enables emotionally intelligent artificial intelligence. While SER is challenging in general, it is particularly difficult for low-resource languages such as Urdu. This study investigates Urdu SER in a cross-corpus setting, an area that has remained largely unexplored. We employ a cross-corpus evaluation framework across three different Urdu emotional speech datasets to test model generalization. Two standard domain-knowledge based acoustic feature sets, eGeMAPS and ComParE, are used to represent speech signals as feature vectors which are then passed to Logistic Regression and Multilayer Perceptron classifiers. Classification performance is assessed using unweighted average recall (UAR) whilst considering class-label imbalance. Results show that Self-corpus validation often overestimates performance, with UAR exceeding cross-corpus evaluation by up to 13%, underscoring that cross-corpus evaluation offers a more realistic measure of model robustness. Overall, this work emphasizes the importance of cross-corpus validation for Urdu SER and its implications contribute to advancing affective computing research for underrepresented language communities.
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