一切都好吗，奶奶？基于声学和语言建模的鲁棒老年人语音情感识别
Is Everything Fine, Grandma? Acoustic and Linguistic Modeling for Robust Elderly Speech Emotion Recognition
摘要：
老年人情感识别的声学和语言分析是一个研究不足且具有挑战性的研究方向，但对于为老年人创建数字助理以及为精神保健目的在住宅中对老年人进行非侵入式远程监控至关重要。本文介绍了我们对INTERSPEECH 2020计算副语言学挑战赛（ComparE）-老年人情感子挑战赛的贡献，该挑战赛由唤醒和效价识别的两个三元分类任务组成。我们提出了一个双模态框架，这些任务分别使用最先进的声学和语言特征进行建模。在这项研究中，我们证明了利用特定于任务的词典和资源可以提高语言模型的性能，当标记数据量很小时。观察各种模型的开发和测试集性能之间的高度不匹配，我们还提出了替代的训练和决策融合策略，以更好地估计和提高泛化性能。
英文摘要：
Acoustic and linguistic analysis for elderly emotion recognition is an under-studied and challenging research direction, but essential for the creation of digital assistants for the elderly, as well as unobtrusive telemonitoring of elderly in their residences for mental healthcare purposes. This paper presents our contribution to the INTERSPEECH 2020 Computational Paralinguistics Challenge (ComParE) - Elderly Emotion Sub-Challenge, which is comprised of two ternary classification tasks for arousal and valence recognition. We propose a bi-modal framework, where these tasks are modeled using state-of-the-art acoustic and linguistic features, respectively. In this study, we demonstrate that exploiting task-specific dictionaries and resources can boost the performance of linguistic models, when the amount of labeled data is small. Observing a high mismatch between development and test set performances of various models, we also propose alternative training and decision fusion strategies to better estimate and improve the generalization performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
