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首页> 外文会议>Conference on Intermountain Engineering, Technology and Computing >A Speech Emotion Recognition Solution-based on Support Vector Machine for Children with Autism Spectrum Disorder to Help Identify Human Emotions
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A Speech Emotion Recognition Solution-based on Support Vector Machine for Children with Autism Spectrum Disorder to Help Identify Human Emotions

机译：基于支持向量机的自闭症谱系障碍儿童语音情感识别解决方案，用于识别人类情感

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摘要

Children who fall into the autism spectrum have difficulty communicating with others. In this work, a speech emotion recognition model has been developed to help children with Autism Spectrum Disorder (ASD) identify emotions in social interactions. The model is created using the Python programming language to develop a machine learning model based on the Support Vector Machine (SVM). SVM has proven to yield high accuracies when classifying inputs in speech processing. Individual audio databases are specifically designed to train models for the emotion recognition task. One such speech corpus is the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), which is used to train the model in this work. Acoustic feature extraction will be part of the pre-processing step utilizing Python libraries. The libROSA library is used in this work. The first 26 Mel-frequency Cepstral Coefficients (MFCCs) and the zero-crossing rate (ZCR) are extracted and used as the acoustic features to train the machine learning model. The final SVM model provided a test accuracy of 77%. This model also performed well when significant background noise was introduced to the RAVDESS audio recordings, for which it yielded a test accuracy of 64%.
机译：属于自闭症谱系的儿童难以与他人交流。在这项工作中，语音情感识别模型已经开发出来，可以帮助患有自闭症谱系障碍（ASD）的儿童识别社交互动中的情感。该模型是使用Python编程语言创建的，用于基于支持向量机（SVM）开发机器学习模型。在对语音处理中的输入进行分类时，SVM已被证明具有很高的准确性。各个音频数据库经过专门设计，可以训练情感识别任务的模型。这样的语音语料库是Ryerson情感语音和歌曲的视听数据库（RAVDESS），用于在这项工作中训练模型。声音特征提取将成为使用Python库的预处理步骤的一部分。在这项工作中使用了libROSA库。提取前26个梅尔频率倒谱系数（MFCC）和过零率（ZCR），并将其用作声学特征以训练机器学习模型。最终的SVM模型提供了77％的测试准确性。当将大量背景噪声引入RAVDESS音频记录时，该模型也表现良好，其测试精度为64％。

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