Margaret Lech

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The properties of acoustic speech have previously been investigated as possible cues for depression in adults. However, these studies were restricted to small populations of patients and the speech recordings were made during patients' clinical interviews or fixed-text reading sessions. Symptoms of depression often first appear during adolescence at a time(More)
A novel method for facial expression recognition from sequences of image frames is described and tested. The expression recognition system is fully automatic, and consists of the following modules: face detection, maximum arousal detection, feature extraction, selection of optimal features, and facial expression recognition. The face detection is based on(More)
We present new methods that extract characteristic features from speech magnitude spectrograms. Two of the presented approaches have been found particularly efficient in the process of automatic stress and emotion classification. In the first approach, the spectrograms are sub-divided into ERB frequency bands and the average energy for each band is(More)
With suicidal behavior being linked to depression that starts at an early age of a person's life, many investigators are trying to find early tell-tale signs to assist psychologists in detecting clinical depression through acoustic analysis of a patient's speech. The purpose of this paper was to study the effectiveness of Mel frequency cepstral coefficients(More)
An efficient automatic facial expression recognition method is proposed. The method uses a set of characteristic features obtained by averaging the outputs from the Gabor filter bank with 5 frequencies and 8 different orientations, and then further reducing the dimensionality by the means of principal component analysis. The performance of the proposed(More)
Previous studies of an automated detection of Major Depression in adolescents based on acoustic speech analysis identified the glottal and the Teager Energy features as the strongest correlates of depression. This study investigates the effectiveness of these features in an early prediction of Major Depression in adolescents using a fully automated speech(More)
In this paper, we report the influence that classification accuracies have in speech analysis from a clinical dataset by adding acoustic low-level descriptors (LLD) belonging to prosodic (i.e. pitch, formants, energy, jitter, shimmer) and spectral features (i.e. spectral flux, centroid, entropy and roll-off) along with their delta (Δ) and delta-delta(More)
This paper studies the effectiveness of speech contents for detecting clinical depression in adolescents. We also evaluated the performances of acoustic features such as Mel frequency cepstral coefficients (MFCC), short time energy (Energy), zero crossing rate (ZCR) and Teager energy operator (TEO) using Gaussian mixture models for depression detection. A(More)