We provide a supervised speech-independent voice recognition technique in this paper. In the feature extraction stage we propose a mel-cepstral based approach. Our feature vector classification method uses a special nonlinear metric, derived from the Hausdorff distance for sets, and a minimum mean distance classifier. Keywords—Text-independent speaker recognition, mel cepstral analysis, speech ...

Speech perception experiments tell us a great deal about which factors affect human performance and behavior. In particular many experiments indicate that the signal-to-noise ratio spectrum is an important factor, indeed the signal-to-noise ratio spectrum is the basis of the Articulation Index, a standard measure of “speech channel capacity.” In this paper we compare speech recognition performa...

objective: diabetes is one of the most common metabolic diseases. earlier diagnosis of diabetes and treatment of hyperglycemia and related metabolic abnormalities is of vital importance. diagnosis of diabetes via proper interpretation of the diabetes data is an important classification problem. classification systems help the clinicians to predict the risk factors that cause the diabetes or pre...

In this paper, we proposed new speech features using independent component analysis to human speeches. When independent component analysis is applied to speech signals for efficient encoding the adapted basis functions resemble Gabor-like features. Trained basis functions have some redundancies, so we select some of the basis functions by reordering method. The basis functions are almost ordere...