In this paper our main aim to provide the difference between cepstral and non-cepstral feature extraction techniques. Here we try to cover-up most of the comparative features of Mel Frequency Cepstral Coefficient and prosodic features. In speaker recognition, there are two type of techniques are available for feature extraction: Short-term features i.e. Mel Frequency Cepstral Coefficient (MFCC)...

This paper is concerned with the development of Back-propagation Neural Network for Bangla Speech Recognition. In this paper, ten bangla digits were recorded from ten speakers and have been recognized. The features of these speech digits were extracted by the method of Mel Frequency Cepstral Coefficient (MFCC) analysis. The mfcc features of five speakers were used to train the network with Back...

This paper motivates the use of Dynamic Mel-Frequency Cepstral Coefficient (DMFCC) feature and combination of DMFCC and MFCC features for robust language and text-independent speaker identification. MFCC feature, modeled on the human auditory system has been the widely used feature for speaker recognition because of its less vulnerability to noise perturbation and little session variability. Bu...

Background: The mel-frequency cepstral coefficients (MFCC) are relied for their capability to identify pathological speech. The literature suggests that triangular mel-filters that are used in the MFCC calculation provide an approximation of the human auditory perception. This approximation allows quantifying the clinician’s perception of the intelligibility of the patient’s speech that allows ...

The main bearing is the core component of gas-fired generator, and its reliability directly affects stability whole system. Therefore, it great significance to study fault diagnosis generator. In based on vibration signal, how extract signature features effectively key achieving accurate diagnosis. Based extracting faults, classify efficiently another this, we propose a method Mel frequency cep...

A K-Nearest Neighbour Algorithm involving Mel-Frequency Cepstral Coefficients (MFCCs) is provided to perform Speech signal feature extraction for the task of speaker accent recognition. Mel-Frequency Cepstral Coefficient is effectively used to perform the feature extraction of the input signal. For each input signal the mean of the MFCC matrix is used for pattern recognition .The K-nearest neig...

Automatic Speech Recognition (ASR) technology is a way to interface with computer. In this paper we describe speech recognition technique using multiple codebooks of MFCC derived features. The proposed algorithm is useful in detecting isolated words of speech. In this algorithm we first create database i.e. codebook by calculating mel frequency cepstral coefficient first and then codeword for e...

Conventional automatic speaker verification systems are based on cepstral features like Mel-scale Frequency Cepstrum Coefficient (MFCC), or Linear Predictive Cepstrum Coefficient (LPCC). Recent published works showed that the use of complementary features can significantly improve the system performances. In this paper, we propose to use an evolution strategy to optimize the complementarity of ...

We introduce a set of speaker dependent features derived from the positions of vowels in Mel-Frequency Cepstral Coefficient (MFCC) space relative to a reference vowel. The MFCCs for a particular speaker are transformed using simple operations into features that can be used to classify vowels from a common reference point. Classification performance of vowels using Gaussian Mixture Models (GMMs)...

In this paper, we apply a discriminative weight training to a support vector machine (SVM) based gender identification. In our approach, the gender decision rule is derived by the SVM incorporating the optimally weighted mel-frequency cepstral coefficient (MFCC) based on a minimum classification error (MCE) method which is different from the previous works in that optimal weights are differentl...