in this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. then, for optimizing the initial feature vector, a genetic algorithm is proposed. some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and k-nearest neig...

Neha Chauhan Birla Institute of Technology, Mesra, Ranchi Abstract— Speaker Recognition is the computing task of validating a user’s claimed identity using speech characteristics. Main objective of speech recognition system is to communication with a device through our voice. Mel frequency Cepstral Coefficient (MFCC) features are combined with pitch and root mean square values and tested for im...

This paper describes a speaker verification system which uses two complementary acoustic features: Mel-frequency cepstral coefficients (MFCC) and wavelet octave coefficients of residues (WOCOR). While MFCC characterizes mainly the spectral envelope, or the formant structure of the vocal tract system, WOCOR aims at representing the spectro-temporal characteristics of the vocal source excitation....

This work examines the utility of formant frequencies and their energies in acoustic-to-articulatory inversion. For this purpose, formant frequencies and formant spectral amplitudes are automatically estimated from audio, and are treated as observations for the purpose of estimating electromagnetic articulography (EMA) coil positions. A mixture Gaussian regression model with mel-frequency cepst...

Identical acoustic features like Mel frequency cepstral Coefficients (MFCC)and Linear predictive cepstral coefficients (LPCC) are being widely used for different tasks like speech recognition and speaker recognition, whereas the requirement of speaker recognition is different than that of speech recognition. In MFCC feature representation, the Mel frequency scale is used to get a high resolutio...

Performance of an automatic speech recognition (ASR) system tends to be dramatically degraded in the presence of impulsive noise. In the previous work [1], we proposed flooring the observation probability (FOP) to compensate the adverse effect of impulsive noise on sensitive dimensions of Mel-frequency cepstral coefficient (MFCC) features. Linear prediction cepstral coefficient (LPCC) is anothe...

Speech recognition is of an important contribution in promoting new technologies in human computer interaction. Today, there is a growing need to employ speech technology in daily life and business activities. However, speech recognition is a challenging task that requires different stages before obtaining the desired output. Among automatic speech recognition (ASR) components is the feature ex...

Mel-frequency cepstral coefficients are widely used as the feature for speech recognition. In MFCC extraction process, the spectrum, obtained by Fourier transform of input speech signal is divided by mel-frequency bands, and each ban energy is extracted for the each frequency band. The coefficients are extracted by the discrete cosine transform of the obtained band energy. In this paper, we cal...

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...