跨语料库多语言语音情感识别：阿姆哈拉语与其他语言的对比
Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages
AI总结：
针对低资源语言语音情感识别缺训练数据的问题，该研究以阿姆哈拉语为核心，对比多语言跨语料迁移效果，发现多源语言训练优于单源，为低资源SER提供了有效策略。
摘要：
在传统语音情感识别（Speech emotion recognition, SER）任务中，针对特定语言的分类器是基于该语言已有的数据集训练的。但当某一语言缺乏训练数据时，可以使用其他语言的数据作为替代。我们针对阿姆哈拉语、英语、德语和乌尔都语开展了跨语言和多语言SER实验。对于阿姆哈拉语，我们使用了自行构建并公开的阿姆哈拉语语音情感数据集（Amharic Speech Emotion Dataset, ASED）；英语、德语和乌尔都语则分别采用已有的RAVDESS、EMO-DB和URDU数据集。我们参照已有研究，将所有数据集的标签统一映射为积极、消极两类，从而可以直接对比不同语言上的性能，也能组合多种语言进行训练和测试。\n实验1开展了单语言SER测试，使用AlexNet、VGGE（本文提出的VGG变体）和ResNet50三个分类器。三个模型的平均结果显示，ASED与RAVDESS的表现非常接近，说明阿姆哈拉语与英语的SER难度相当；德语SER难度更高，乌尔都语SER难度更低。\n实验2采用单语言训练、另一语言测试的设置，对阿姆哈拉语<->德语、阿姆哈拉语<->英语、阿姆哈拉语<->乌尔都语三个语言对的双向迁移均进行了测试。以阿姆哈拉语为目标语言的结果表明，使用英语或德语作为源语言能取得最佳效果。\n实验3使用多种非阿姆哈拉语进行训练，再在阿姆哈拉语上测试。实验获得的最佳准确率比实验2的最佳准确率高出数个百分点，说明使用2到3种非阿姆哈拉语训练的效果优于仅使用1种非阿姆哈拉语。\n总体而言，结果表明当某一语言的资源匮乏时，跨语言和多语言训练是训练SER分类器的有效策略。
英文摘要：
In a conventional Speech emotion recognition (SER) task, a classifier for a given language is trained on a pre-existing dataset for that same language. However, where training data for a language does not exist, data from other languages can be used instead. We experiment with cross-lingual and multilingual SER, working with Amharic, English, German and URDU. For Amharic, we use our own publicly-available Amharic Speech Emotion Dataset (ASED). For English, German and Urdu we use the existing RAVDESS, EMO-DB and URDU datasets. We followed previous research in mapping labels for all datasets to just two classes, positive and negative. Thus we can compare performance on different languages directly, and combine languages for training and testing. In Experiment 1, monolingual SER trials were carried out using three classifiers, AlexNet, VGGE (a proposed variant of VGG), and ResNet50. Results averaged for the three models were very similar for ASED and RAVDESS, suggesting that Amharic and English SER are equally difficult. Similarly, German SER is more difficult, and Urdu SER is easier. In Experiment 2, we trained on one language and tested on another, in both directions for each pair: Amharic<->German, Amharic<->English, and Amharic<->Urdu. Results with Amharic as target suggested that using English or German as source will give the best result. In Experiment 3, we trained on several non-Amharic languages and then tested on Amharic. The best accuracy obtained was several percent greater than the best accuracy in Experiment 2, suggesting that a better result can be obtained when using two or three non-Amharic languages for training than when using just one non-Amharic language. Overall, the results suggest that cross-lingual and multilingual training can be an effective strategy for training a SER classifier when resources for a language are scarce.
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。
