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Speech signal quality is of fundamental importance for accurate speaker identification. The reliability of a speech biometry system, in fact, is known to depend on the amount of material available, in particular on the number of vowels present in the sequence being analysed and on the quality of the signal. This paper highlights the performance of two Signal-to-Noise Ratio (SNR) estimation methods...
The paper presents the analysis of the robustness of an attributes selection method applied to speech emotion recognition. The features used were extracted by the front-end ETSI Aurora eXtended of a mobile terminal in compliance with the ETSI ES 202 211 V1.1.1 standard. On the basis of the time trend of these parameters, over 3700 statistical attributes were extracted to characterize semantic units...
The paper presents the study and the performance results of a system for emotion classification using the architecture of a Distributed Speech Recognition System (DSR). The parameters used were extracted by the front-end ETSI Aurora eXtended of a mobile terminal in compliance with the ETSI ES 202 211 V1.1.1 standard. On the basis of the time trend of these parameters, over 3800 statistical parameters...
The recognition of emotional states is a relatively new technique in the field of machine learning. The paper presents the study and the performance results of a system for emotion classification using the architecture of a distributed speech recognition system (DSR). The features used were extracted by the front-end ETSI Aurora eXtended of a mobile terminal in compliance with the ETSI ES 202-211...
The paper presents an adaptive system for voiced/unvoiced (V/UV) speech detection in the presence of background noise. Genetic algorithms were used to select the features that offer the best V/UV detection according to the output of a background noise classifier (NC) and a signal to noise ratio estimation (SNRE) system. The system was implemented and the tests performed using the TIMIT speech corpus...
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