面向低资源老年人语音情感识别的跨语言跨年龄组适应
Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition
AI总结：
针对语音情感识别偏向英语成年人的问题，本研究构建了跨语言跨年龄基准与数据集，发现跨语言推理不适用，但跨组数据增强结合语言距离能有效正则化模型并影响可迁移性。
摘要：
语音情感识别在人机交互中起着至关重要的作用。然而，大多数语音情感识别研究偏向于说英语的成年人，这阻碍了其在不同语言和年龄组的其他人群中的适用性。在这项工作中，我们分析了情感识别在三种不同语言——英语、普通话和粤语；以及2个不同年龄组——成年人和老年人之间的可迁移性。为了进行实验，我们开发了一个面向成年人和老年人的英-普通话语音情感基准BiMotion，以及一个粤语语音情感数据集YueMotion。本研究得出结论，不同的语言和年龄组需要特定的语音特征，因此跨语言推理是一种不合适的方法。然而，跨组数据增强仍然有利于正则化模型，其中语言距离对跨语言可迁移性有显著影响。我们在https://github.com/HLTCHKUST/elderly_ser公开发布了我们的代码。
英文摘要：
Speech emotion recognition plays a crucial role in human-computer interactions. However, most speech emotion recognition research is biased toward English-speaking adults, which hinders its applicability to other demographic groups in different languages and age groups. In this work, we analyze the transferability of emotion recognition across three different languages--English, Mandarin Chinese, and Cantonese; and 2 different age groups--adults and the elderly. To conduct the experiment, we develop an English-Mandarin speech emotion benchmark for adults and the elderly, BiMotion, and a Cantonese speech emotion dataset, YueMotion. This study concludes that different language and age groups require specific speech features, thus making cross-lingual inference an unsuitable method. However, cross-group data augmentation is still beneficial to regularize the model, with linguistic distance being a significant influence on cross-lingual transferability. We release publicly release our code at https://github.com/HLTCHKUST/elderly_ser.
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。
