Human Emotion Recognition Based on Machine Learning Algorithms with low Resource Environment

IF 1.8 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ACM Transactions on Asian and Low-Resource Language Information Processing Pub Date : 2024-01-26 DOI:10.1145/3640340

Asha P., Hemamalini V., Poongodaia., Swapna N., Soujanya K. L. S., Vaishali Gaikwad (Mohite)

引用次数: 0

Abstract

It is difficult to discover significant audio elements and conduct systematic comparison analyses when trying to automatically detect emotions in speech. In situations when it is desirable to reduce memory and processing constraints, this research deals with emotion recognition. One way to achieve this is by reducing the amount of features. In this study, propose "Active Feature Selection" (AFS) method and compares it against different state-of-the-art techniques. According to the results, smaller subsets of features than the complete feature set can produce accuracy that is comparable to or better than the full feature set. The memory and processing requirements of an emotion identification system will be reduced, which can minimise the hurdles to using health monitoring technology. The results show by using 696 characteristics, the AFS technique for emobase yields a Unweighted average recall (UAR) of 75.8%.

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低资源环境下基于机器学习算法的人类情感识别

在试图自动检测语音中的情绪时，很难发现重要的音频元素并进行系统的对比分析。在希望减少记忆和处理限制的情况下，这项研究涉及情感识别。实现这一目标的方法之一是减少特征数量。本研究提出了 "主动特征选择"（AFS）方法，并将其与不同的先进技术进行了比较。结果表明，比完整特征集更小的特征子集所产生的准确率可与完整特征集相媲美，甚至更好。情绪识别系统对内存和处理的要求也会降低，这可以最大限度地减少使用健康监测技术的障碍。结果表明，通过使用 696 个特征，针对 emobase 的 AFS 技术的非加权平均召回率（UAR）为 75.8%。

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来源期刊

ACM Transactions on Asian and Low-Resource Language Information Processing Computer Science-General Computer Science

CiteScore

3.60

自引率

15.00%

发文量

241

期刊介绍： The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to: -Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc. -Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc. -Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition. -Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc. -Machine Translation involving Asian or low-resource languages. -Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc. -Information Extraction and Filtering: including automatic abstraction, user profiling, etc. -Speech processing: including text-to-speech synthesis and automatic speech recognition. -Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc. -Cross-lingual information processing involving Asian or low-resource languages. -Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.