Mel frequency cepstral coefficients (MFCC) are the most widely used speech features in automatic speech recognition systems, primarily because the coefficients fit well with the assumptions used in hidden Markov models and because of the superior noise robustness of MFCC over alternative feature sets such as linear prediction-based coefficients. The authors have recently introduced human factor...

This work presents an application of Fundamental Frequency (Pitch), Linear Predictive Cepstral Coefficient (LPCC) and Mel Frequency Cepstral Coefficient (MFCC) in identification of sex of the speaker in speech recognition research. The aim of this article is to compare the performance of these three methods for identification of sex of the speakers. A successful speech recognition system can he...

This work presents an application of Fundamental Frequency (Pitch), Linear Predictive Cepstral Coefficient (LPCC) and Mel Frequency Cepstral Coefficient (MFCC) in identification of sex of the speaker in speech recognition research. The aim of this article is to compare the performance of these three methods for identification of sex of the speakers. A successful speech recognition system can he...

Novel speech features calculated from third-order statistics of subband-filtered speech signals are introduced and studied for robust speech recognition. These features have the potential to capture nonlinear information not represented by cepstral coefficients. Also, because the features presented in this paper are based on the third-order moments, they may be more immune to Gaussian noise tha...

In the paper recently proposed Human Factor Cepstral Coefficients (HFCC) are used to automatic recognition of pathological phoneme pronunciation in speech of impaired children and efficiency of this approach is compared to application of the standard Mel-Frequency Cepstral Coefficients (MFCC) as a feature vector. Both dynamic time warping (DTW), working on whole words or embedded phoneme patter...

In the present work we overview some recently proposed discrete Fourier transform (DFT)and discrete wavelet packet transform (DWPT)-based speech parameterization methods and evaluate their performance on the speech recognition task. Specifically, in order to assess the practical value of these less studied speech parameterization methods, we evaluate them in a common experimental setup and comp...

Speech is the effective form of communication between human and its environment. Dysarthria is a motor speech disorder in which the person lacks the control over articulators used for speech production. Speech accuracy is the outcome of well-timed and coordinated activities of the articulators and other related neuro muscular feature. In this paper, Speech utterance is converted into a phone se...

This paper proposes an unsupervised method for improving the automatic speaker segmentation performance by combining the evidence from residual phase (RP) and mel frequency cepstral coefficients (MFCC). This method demonstrates the complementary nature of speaker specific information present in the residual phase in comparison with the information present in the conventional MFCC. Moreover this...

In this paper we propose a filter bank structure derived by using admissible wavelet packet transform. These filters have Mel scale spacing and have an advantage of easy implementation with higher resolution in time-frequency domain because of wavelet transform. The features are obtained by first calculating the energy in each filter band and then applying the Discrete Cosine Transform (DCT) to...

This paper presents the work on Automatic Speech Recognition of Urdu language, using a comparative analysis for Discrete Wavelets Transform (DWT) based features and Mel Frequency Cepstral Coefficients (MFCC). These features have been extracted for one hundred isolated words of Urdu, each word uttered by ten different speakers. The words have been selected from the most frequently used words of ...