Yazid Attabi

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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)
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 fiveclass emotion detection. For the two-class problem, the best performance is obtained by logistic regression fusion of three systems. These systems use shortand long-term speech(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 paper presents an improved version of anchor model applied to solve the two-class classification tasks of the INTERSPEECH 2012 speaker trait Challenge. To build the anchor model space of each task, we include the class models of all tasks. The introduction of within-class covariance normalization (WCCN) applied to the log-likelihood scores of the(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)
In this paper we present a new framework for emotion recognition from speech based on a similarity concept called Weighted Ordered Classes-Nearest Neighbors (WOC-NN). Unlike the k-nearest neighbor, an instance-similarity based method; WOC-NN computes similarities between a test instance and a class pattern of each emotion class. An emotion class pattern is(More)
The goal of speech emotion recognition (SER) is to identify the emotional or physical state of a human being from his or her voice. One of the most important things in a SER task is to extract and select relevant speech features with which most emotions could be recognized. In this paper, we present a smoothed nonlinear energy operator (SNEO)-based(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)