The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Three new methods of feature extraction based on time-frequency analysis of speech are presented and compared. In the first approach, speech spectrograms were passed through a bank of 12 log-Gabor filters and the outputs are averaged. In the second approach, the spectrograms were sub-divided into ERB frequency bands and the average energy for each band is calculated. In the third approach, wavelet...
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 different vector quantization algorithms, namely...
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 calculated. In the second approach, the spectrograms...
This paper presents a new system for automatic stress detection in speech. In the process of feature extraction speech spectrograms were used as the primary features. The sigma-pi neuron cells were then employed to derive the secondary features. The analysis was performed at three alternative sets of analytical frequency bands: critical bands, Bark scale bands and equivalent rectangular bandwidth...
The speech signal is an important tool for conveying information between humans; at the same time, it is an indicator of a speaker's emotions. In this paper, the automatic identification of affect from speech containing spontaneously expressed (not acted) emotions within different environments was investigated. The teager energy operator-perceptual wavelet packet (TEO-PWP) features as well as the...
This paper investigates automatic affect classification in spontaneous speech within normal and clinical family environments. The data base used in this study comprised speech recordings of parents of depressed adolescents (19 fathers and 20 mothers) and parents of non-depressed adolescents (25 fathers and 7 mothers). The speech data were recorded during natural parent-child conversations. Five emotional...
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 (MFCCs) in capturing the overall mental...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.