Method for emotion recognition based on minimum classification error
First Claim
1. A method for emotion recognition based on a minimum classification error, the method comprising:
- extracting a feature vector for emotion recognition based on a voice signal generated from a speaker and a galvanic skin response of the speaker, the feature vector for emotion recognition including a voice signal feature vector containing information extracted from the voice signal of the speaker and a galvanic skin response feature vector extracted from galvanic skin response of the speaker;
classifying a neutral emotion using a Gaussian mixture model based on the extracted feature vector for emotion recognition; and
classifying other emotions except the previously classified neutral emotion using the Gaussian Mixture Model to which a discriminative weight for minimizing the loss function of a classification error for the feature vector for emotion recognition is applied, wherein the emotions are classified by comparing a likelihood ratio with a threshold value, and the likelihood ratio is obtained from the Gaussian Mixture Model modified by the discriminative weight.
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Abstract
Disclosed herein is a method for emotion recognition based on a minimum classification error. In the method, a speaker'"'"'s neutral emotion is extracted using a Gaussian mixture model (GMM), other emotions except the neutral emotion are classified using the Gaussian Mixture Model to which a discriminative weight for minimizing the loss function of a classification error for the feature vector for emotion recognition is applied. In the emotion recognition, the emotion recognition is performed by applying a discriminative weight evaluated using the Gaussian Mixture Model based on minimum classification error to feature vectors of the emotion classified with difficult, thereby enhancing the performance of emotion recognition.
13 Citations
6 Claims
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1. A method for emotion recognition based on a minimum classification error, the method comprising:
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extracting a feature vector for emotion recognition based on a voice signal generated from a speaker and a galvanic skin response of the speaker, the feature vector for emotion recognition including a voice signal feature vector containing information extracted from the voice signal of the speaker and a galvanic skin response feature vector extracted from galvanic skin response of the speaker; classifying a neutral emotion using a Gaussian mixture model based on the extracted feature vector for emotion recognition; and classifying other emotions except the previously classified neutral emotion using the Gaussian Mixture Model to which a discriminative weight for minimizing the loss function of a classification error for the feature vector for emotion recognition is applied, wherein the emotions are classified by comparing a likelihood ratio with a threshold value, and the likelihood ratio is obtained from the Gaussian Mixture Model modified by the discriminative weight. - View Dependent Claims (2, 3, 4, 5, 6)
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Specification