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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)
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)
The introduction of Gaussian Mixture Models (GMMs) in the field of speaker verification has led to very good results. This paper illustrates an evolution in state-of-the-art Speaker Verification by highlighting the contribution of recently established information theoretic based vector quantization technique. We explore the novel application of three(More)
The automatic recognition and classification of speech under stress has applications in behavioural and mental health sciences, human to machine communication and robotics. The majority of recent studies are based on a linear model of the speech signal. In this study, the nonlinear Teager Energy Operator (TEO) analysis was used to derive the classification(More)
We proposed a framework to detect the video contents of depressed and non-depressed subjects. First we characterized the expressed emotions in the video stream using Gabor wavelet features extracted at the facial landmarks which were detected using landmark model matching algorithm. Depressed and non-depressed class models were constructed using Gaussian(More)
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)
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)
The speech of cleft palate (CP) patients has typical characteristics. Hypernasality and low speech intelligibility are the primary characteristics for CP speech. In this work, an automatic evaluation of different levels of hypernasality and speech intelligibility algorithm for CP speech was proposed, in order to provide an objective tool for speech(More)