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首页> 外文期刊>International journal of synthetic emotions >Feature Selection for GUMI Kernel-Based SVM in Speech Emotion Recognition
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Feature Selection for GUMI Kernel-Based SVM in Speech Emotion Recognition

机译：语音情感识别中基于GUMI内核的SVM的特征选择

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摘要

Speech emotion recognition is the indispensable requirement for efficient human machine interaction. Most modern automatic speech emotion recognition systems use Gaussian mixture models (GMM) and Support Vector Machines (SVM). GMM are known for their performance and scalability in the spectral modeling while SVM are known for their discriminatory power. A GMM-supervector characterizes an emotional style by the GMM parameters (mean vectors,' covariance matrices, and mixture weights). GMM-supervector SVM benefits from both GMM and SVM frameworks. In this paper, the GMM-UBM mean interval (GUMI) kernel based on the Bhattacharyya distance is successfully used. CFSSubsetEval combined with Best first algorithm and Greedy stepwise were also utilized on the supervectors space in order to select the most important features. This framework is illustrated using Mel-frequency cepstral (MFCC) coefficients and Perceptual Linear Prediction (PLP) features on two different emotional databases namely the Surrey Audio-Expressed Emotion and the Berlin Emotional speech Database.
机译：语音情感识别是有效的人机交互必不可少的条件。大多数现代的自动语音情感识别系统都使用高斯混合模型（GMM）和支持向量机（SVM）。 GMM因其在频谱建模中的性能和可伸缩性而闻名，而SVM因其区分能力而闻名。 GMM超向量通过GMM参数（均值向量，协方差矩阵和混合权重）来表征情感风格。 GMM-supervector SVM受益于GMM和SVM框架。本文成功地使用了基于Bhattacharyya距离的GMM-UBM平均间隔（GUMI）核。 CFSSubsetEval与Best first算法和Greedy stepwise相结合，还用于超向量空间，以选择最重要的特征。使用梅尔频率倒谱（MFCC）系数和感知线性预测（PLP）功能在两个不同的情感数据库（萨里音频表达情感和柏林情感语音数据库）上说明了此框架。

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