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Emotion recognition in speech using MFCC with SVM, DSVM and auto-encoder

机译：使用带有SVM，DSVM和自动编码器的MFCC进行语音情感识别

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摘要

Emotions recognition from speech is one of the most important sub domains in the field of signal processing. In this work, our system is a two-stage approach, namely feature extraction and classification engine. Firstly, two sets of feature are investigated which are: 39 Mel-frequency Cepstral Coefficient (MFCC) coefficients and 65 MFCC features extracted based on the work of [20]. Secondly, we use the Support Vector Machine (SVM) as the main classifier engine since it is the most common technique in the field of speech recognition. Besides that, we investigate the importance of the recent advances in machine learning including the deep kernel learning, as well as the various types of auto-encoder (the basic auto-encoder and the stacked auto-encoder). A large set of experiments are conducted on the SAVEE audio database. The experimental results show that DSVM method outperforms the standard SVM with a classification rate of 69.84% and 68.25% using 39 MFCC, respectively. Additionally, the auto-encoder method outperforms the standard SVM, yielding a classification rate of 73.01%.
机译：来自语音的情感识别是信号处理领域中最重要的子领域之一。在这项工作中，我们的系统分为两个阶段，即特征提取和分类引擎。首先，研究了两组特征：基于[20]的工作提取的39个梅尔频率倒谱系数（MFCC）系数和65个MFCC特征。其次，我们使用支持向量机（SVM）作为主要的分类器引擎，因为它是语音识别领域中最常见的技术。除此之外，我们还研究了机器学习的最新进展的重要性，包括深度内核学习以及各种类型的自动编码器（基本自动编码器和堆叠式自动编码器）。在SAVEE音频数据库上进行了大量实验。实验结果表明，使用39 MFCC，DSVM方法的分类率分别为69.84 \％和68.25 \％，优于标准SVM。此外，自动编码器方法优于标准SVM，分类率为73.01 \％。

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