In this work a multilingual speaker identification system is proposed. The feature extraction techniques employed in the system extract Mel frequency cepstral coefficient (MFCC), delta mel frequency cepstral coefficient (DMFCC) and format frequency. The feature selection is done using hybrid model of particle swarm optimizatiom (PSO) and Genetic Algorithm (GA). We have used Back Propagation (BP...

Recently, using prosodic information such as pitch and energy for speaker recognition has attracted much attention. However, these kinds of systems yield performance much worse than the traditional cepstral based systems. Limited performance improvement can be achieved when combining the two kinds of systems. In this paper, we present a new approach for speaker recognition, which uses the proso...

Speech recognition systems perform poorly on speech degraded by even simple effects such as linear filtering and additive noise. One solution to this problem is to modify the probability density function (PDF) of clean speech to account for the effects of the degradation. However, even for the case of linear filtering and additive noise, it is extremely difficult to do this analytically. Previo...

Vergence ability is an important visual behavior observed on living creatures when they use vision to interact with the environment. The notion of active observer is equally useful for robotic vision systems on tasks like object tracking, fixation and 3D environment structure recovery. Humanoid robotics are a potential playground for such behaviors. This paper describes the implementation of a ...

We describe four improvements to a prosody SVM system, including a new method based on textand part-of-speechconstrained prosodic features. The improved system shows remarkably good performance on NIST SRE06 data, reducing the error rate of an MLLR system by as much as 23% after combination. In addition, an N -best system analysis using eight systems reveals that the prosody SVM is the third an...

The proposed Missing Feature Linear-Frequency Cepstral Coefficients (MF-LFCC) is a noise robust cepstral feature that transforms both clean and noisy signals into a similar representation. Unlike conventional Missing Feature Techniques, the MF-LFCC does not require the substitution of spectrogram elements (imputation) or classifier modification (marginalization). To improve the noise mask used ...

The most popular set of parameters used in recognition systems is the me1 frequency cepstral cocfficicnts. While giving generally good results, it remains that the filtering process, as used in the evaluation of these parameters, reduces the signal resolution in the frequency domain, which can have some impact in discriminating between phonemes. This paper presents a new parameterization approa...

The most popular speech feature extractor used in automatic speech recognition (ASR) systems today is the mel frequency cepstral coefficient (mfcc) algorithm. Introduced in 1980, the filter bank-based algorithm eventually replaced linear prediction cepstral coefficients (lpcc) as the premier front end, primarily because of mfcc’s superior robustness to additive noise. However, mfcc does not app...

In this paper we propose a new fusion technique, termed Joint Cohort Normalization Fusion, where the information fusion is done prior to the likelihood ratio test in a speaker verification system. The performance of the technique is compared against two popular types of fusion: feature vector concatenation and expert opinion fusion, for fusion of Mel Frequency Cepstral Coefficients (MFCC), MFCC...

This paper describes the implementation of two isolated digit recognition techniques and is a comparison between the algorithms implemented. Any digit recognition comprises of mainly two stages feature extraction and similarity evaluation. Here, two feature extraction techniques, namely linear predictive cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are implemented...