Application of Machine Learning Algorithms in Speech Emotion Recognition

Junyi Cao
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Abstract

Speech emotion recognition has been widely used in recent years and has become a heated topic for research. Focused on the convolutional neural network model using spectrograms as input, the CNN-LSTM model based on feature vectors, original speech signal and Log-mel spectrograms, the performance of different models is compared as well as analyzed. The study found that there are some common problems existing in the classification performance of the model. The features and algorithms currently used can effectively distinguish emotions with varied “arousal”, but it is difficult to identify the feelings with similar arousal, among the models. The CNN-LSTM model with Log-mel spectrograms as input achieved the highest accuracy.
机器学习算法在语音情感识别中的应用
语音情感识别近年来得到了广泛的应用,成为研究的热点。重点对以频谱图为输入的卷积神经网络模型、基于特征向量、原始语音信号和Log-mel频谱图的CNN-LSTM模型进行了性能比较和分析。研究发现,该模型的分类性能存在一些共性问题。目前使用的特征和算法可以有效区分具有不同“唤醒”的情绪,但难以在模型中识别具有相似唤醒的情绪。以Log-mel谱图为输入的CNN-LSTM模型获得了最高的精度。
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