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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 study investigates effects of a clinical environment on speaker recognition rates. Two sets of speakers were used: a clinical set containing speech recordings of 70 clinically depressed speakers and a control set containing 68 non-depressed speakers. MFCC characteristic features were used to produce statistical models of speakers using four modeling methods: GMM_EM, GMM_K-means, GMM_LBG, and...
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...
This paper presents an analysis of the error associated with speech after it has been processed using QAM modulation and sent through an IFFT/FFT OFDM transmission system. The main focus will be on how this type of modulation and transmission system will affect the unique features of speech used for speaker recognition and verification systems. Channel deformations can lead to serious errors in speech...
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