This paper introduces and motivates the use of hybrid robust feature extraction technique for spoken language identification (LID) sys tem. The speech recognizers use a parametric form of a signal to get the most important distinguishable features of speech signal for recognition task. In this paper Mel-frequency cepstral coefficients (MFCC), Perceptual linear prediction coefficients (PLP) alon...

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....

In this paper, we investigate the noise-robustness of features based on the cepstral time coefficients (CTC). By cepstral time coefficients, we mean the coefficients obtained from applying the discrete cosine transform to the commonly used mel-frequency cepstral coefficients (MFCC). Furthermore, we apply temporal filters used for computing delta and acceleration dynamic features to the CTC, res...

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...

In the present work we explore the influence of front-end setup on the speech recognition performance. Specifically, we study the dependence between specific parameters of the speech parameterization stage, such as speech frame size and number of Mel-frequency cepstral coefficients (MFCC), and the word error rate (WER). Our comparative evaluation is performed by employing the Sphinx-IV speech r...

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)...

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...

The automatic speaker verification spoofing and countermeasures challenge 2015 provides a common framework for the evaluation of spoofing countermeasures or anti-spoofing techniques in the presence of various seen and unseen spoofing attacks. This contribution proposes a system consisting of amplitude, phase, linear prediction residual, and combined amplitude phase-based countermeasures for the...

This paper describes a hybrid technique for speaker recognition. Speaker recognition is that the method of identifying the person based on characteristics like pitch, tone, Cepstral coefficients in the speech wave. Here DWT and MFCC technique is employed for feature extraction. A mix of two or lot of techniques is named hybrid technique. DWT means divide the speech signal completely into differ...

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...