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Gaussian mixture models (GMMs) and the minimum error rate classifier (i.e. Bayesian optimal classifier) are popular and effective tools for speech emotion recognition. Typically, GMMs are used to model the class-conditional distributions of acoustic features and their parameters are estimated by the expectation maximization (EM) algorithm based on a training data set. Then, classification is performed...
This paper proposes an efficient feature vector classification for Speech Emotion Recognition (SER) in service robots. Since service robots interact with diverse users who are in various emotional states, two important issues should be addressed: acoustically similar characteristics between emotions and variable speaker characteristics due to different user speaking styles. Each of these issues may...
The purpose of this paper is to describe the realization of speech emotion recognition. Generally, text-independent mode has been utilized for speech emotion recognition, hence previous researches have discounted that emotion features vary according to the text or phonemes, though this can distort the classification performance. To overcome this distortion, a framework of speech emotion recognition...
In previous systems of speech emotion recognition, supervised learning are frequently employed to train classifiers on lots of labeled examples. However, the labeling of abundant data requires much time and many human efforts. This paper presents an enhanced co-training algorithm to utilize a large amount of unlabeled speech utterances for building a semi-supervised learning system. It uses two conditionally...
The importance of automatically recognizing emotions from human speech has grown with the increasing role of spoken language interface in man-machine applications. The paper presents a system for the recognition of emotional states based on parameters extracted at the front end of a mobile terminal according to the ETSI ES 202 050 standard. Starting from a vector of various features derived from energy...
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