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In this paper, we describe systems that were developed for the Open Performance Sub-Challenge of the INTERSPEECH 2009 Emotion Challenge. We participate in both two-class and five-class emotion detection. For the two-class problem, the best performance is obtained by logistic regression fusion of three systems. These systems use short-and long-term speech(More)
In this paper, we study the effectiveness of anchor models applied to the multiclass problem of emotion recognition from speech. In the anchor models system, an emotion class is characterized by its measure of similarity relative to other emotion classes. Generative models such as Gaussian Mixture Models (GMMs) are often used as front-end systems to(More)
MFCC (Mel Frequency Cepstral Coefficients) and PLP (Perceptual linear prediction coefficients) or RASTA-PLP have demonstrated good results whether when they are used in combination with prosodic features as suprasegmental (long-term) information or when used stand-alone as segmental (short-time) information. MFCC and PLP feature parameterization aims to(More)
This study represents an extension work of the Weighted Ordered Classes-Nearest Neighbors (WOC-NN), a class-similarity based method introduced in our previous work [1]. WOC-NN computes similarities between a test instance and a class pattern of each emotion class in the likelihood space. An emotion class pattern is a representation of its ranked neighboring(More)
Several methods have recently been proposed to analyze speech and automatically infer the personality of the speaker. These methods often rely on prosodic and other hand crafted speech processing features extracted with off-the-shelf toolboxes. To achieve high accuracy, numerous features are typically extracted using complex and highly parameterized(More)