Speech recognition is a method of finding similarity between two sequences. Various researches have been done on it. In our research, we are trying to achieve the optimal accuracy during the recognition procedure. Here, we are extracting features of the voice sample before filtering it through a noise reduction filter. For each individual, there are number of features are taken using feature ex...

This paper presents a brief survey on Automatic Voice Recognition so as to provide a technological perspective and an appreciation of the fundamental progress that has been accomplished in area of voice communication. The voice is a signal of infinite information. After years of research and development the accuracy of automatic voice recognition remains one of the important research challenges...

Ameliorating the performances of speech recognition system is a challenging problem interesting recent researchers. In this paper, we compare two extraction methods of Mel Frequency Cepstral Coefficients used to represent stressed speech utterances in order to obtain best performances. The first method known as traditional is based on single window (taper) generally the Hamming window and the s...

Several features were compared with regard to recognition performance in a musical instrument recognition system. Both mel-frequency and linear prediction cepstral and delta cepstral coefficients were calculated. Linear prediction analysis was carried out both on a uniform and a warped frequency scale, and reflection coefficients were also used as features. The performance of earlier described ...

In this paper, the performance of several algorithms for the quantization of the mel-generalized cepstral coe cients is studied. First, the objective and subjective performance of two-stage vector quantization (VQ) is measured. It is shown that subjective quality for the mel-generalized cepstral coe cients is higher than that for LSP. Secondly, interframe prediction is introduced in the encodin...

Human Voice is characteristic for an individual. The ability to recognize the speaker by his/her voice can be a valuable biometric tool with enormous commercial as well as academic potential. Commercially, it can be utilized for ensuring secure access to any system. Academically, it can shed light on the speech processing abilities of the brain as well as speech mechanism. In fact, this feature...

This paper describes a new framework for designing speaker recognition systems based on the discriminative feature extraction (DFE) method. We apply a mel-cepstral estimation technique to the feature extractor in a Gaussian mixture model (GMM)-based text-independent speaker identification system. The mel-cepstral estimation technique uses the second-order all-pass warping function for frequency...