Implementation and Comparison of Speech Emotion Recognition System Using Gaussian Mixture Model (GMM) and K- Nearest Neighbor (K-NN) Techniques

Rahul B. Lanjewar; Swarup Mathurkar; Nilesh Patel

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Implementation and Comparison of Speech Emotion Recognition System Using Gaussian Mixture Model (GMM) and K- Nearest Neighbor (K-NN) Techniques

机译：使用高斯混合模型（GMM）和K-最近邻（K-NN）技术的语音情感识别系统的实现和比较

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The kinship between man and machines has become a new trend of technology such that machines now have to respond by considering the human emotional levels. The signal processing and machine learning technologies have boosted the machine intelligence that it gained the capability to understand human emotions. Incorporating the aspects of speech processing and pattern recognition algorithms an intelligent and emotions specific man-machine interaction can be achieved which can be harnessed to design a smart and secure automated home as well as commercial application. This paper emphasizes on implementation of speech emotion recognition system by utilizing the spectral components of Mel Frequency Cepstrum Coefficients (MFCC), wavelet features of speech and the pitch of vocal traces. The different machine learning algorithms used for the classification are Gaussian Mixture Model (GMM) and K- Nearest Neighbour (K-NN) models for the recognition of six emotional categories namely happy, angry, neutral, surprised, fearful and sad from the standard speech database Berlin emotion database (BES) followed by the comparison of the two algorithms for performance analysis which is supported by the confusion matrix.

机译：人与机器之间的亲属关系已成为技术的新趋势，以至于机器现在必须通过考虑人类的情感水平来作出反应。信号处理和机器学习技术提高了机器智能，使其获得了理解人类情感的能力。结合语音处理和模式识别算法的各个方面，可以实现智能的和特定于情感的人机交互，可以将其用于设计智能，安全的自动化家庭以及商业应用。本文重点介绍了利用梅尔频率倒谱系数（MFCC）的频谱分量，语音的小波特征和人声的音高来实现语音情感识别系统的方法。用于分类的不同机器学习算法是高斯混合模型（GMM）和K-最近邻居（K-NN）模型，用于从标准语音中识别六个情感类别，即快乐，愤怒，中性，惊讶，恐惧和悲伤柏林情感数据库（BES），然后比较两种算法进行的性能分析，该算法由混淆矩阵支持。

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《Procedia Computer Science》 |2015年第1期|共8页
作者
Rahul B. Lanjewar; Swarup Mathurkar; Nilesh Patel;
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中图分类计算技术、计算机技术;
关键词
Speech FeaturesEmotionMFCCwaveletpitchK-NNGMMDatabase;

机译：语音功能情感MFCC小波音调K-NNGMM数据库;

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1. Analysis And Identification Of Emotion Specific Features For Speaker Independent Emotion Recognition System Using Gaussian Mixture Models (GMMs) [J] . J. Naga Padmaja, R. RajeswarRao Advances in computational sciences and technology . 2017,第8PTa2343a2506期

机译：基于高斯混合模型（GMM）的独立于说话人的情绪识别系统的情绪特定特征的分析和识别
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机译：基于Gabor滤波器的面部识别系统的可行性和门控制系统FPGA装置中的邻近邻技术
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机译：基于最近邻居的连续语音识别中的快速似然计算技术
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机译：基于DDMFCC向量的卡纳达语情感识别的基于人工神经网络和高斯混合模型的机器学习技术的比较
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机译：逆协方差的混合：高斯混合的协方差建模及其在自动语音识别中的应用。
6. A Comparison of the Spatial Linear Model to Nearest Neighbor (k-NN) Methods for Forestry Applications [O] . Jay M. Ver Hoef, Hailemariam Temesgen -1

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7. Implementation and Comparison of Speech Emotion Recognition System Using Gaussian Mixture Model (GMM) and K- Nearest Neighbor (K-NN) Techniques [O] . Lanjewar Rahul B., Mathurkar Swarup, Patel Nilesh 2015

机译：使用高斯混合模型（GMM）和K-最近邻（K-NN）技术的语音情感识别系统的实现和比较

Implementation and Comparison of Speech Emotion Recognition System Using Gaussian Mixture Model (GMM) and K- Nearest Neighbor (K-NN) Techniques

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