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This paper describes an approach to select the most relevant subspace in Kernel PCA feature space applied on MFCC coefficients for speech recognition. It has been seen that the relevant information about a supervised classification problem is contained in a finite number of leading Kernel PCA components if the Kernel matches the underlying classification problem. In this paper our contribution is...
Line Spectral Pairs Frequencies (LSFs) provide an alternative representation of the linear prediction coefficients. In this paper an investigation is carried out for extracting feature for speaker identification task which is based on perceptual analysis of speech signal and LSF. A modified version of the standard perceptual analysis is applied to obtain better performance. We have extracted the conventional...
The total recognition time as well as the memory requirement in speaker recognition is mainly governed by the number of speakers, the number of frame vectors in the test sequence and the feature dimensionality. The adjacent frame vectors can show similarity in the feature space because of the slow movements of the articulators. Hence efficient frame selection techniques to select non-redundant frames...
Feature extraction is one of the most significant stage in development of a speaker identification (SI) system. Most of the SI systems use mel-frequency cepstral coefficient (MFCC) as a parameter for representing the speech signal into compact form. MFCC are extracted through spectral weighting by a bank of overlapping triangular filters followed by a de-correlation process. Conventionally, discrete...
The amount of speaker specific information in speech signal varies from frame to frame depending on spoken text and environmental conditions. A frame selection at the preprocessing stage can be an added advantage in this context. In pre-quantization (PQ) we select a new sequence of frames Y from the original frames X such that length of Y is less than X. In this paper, we first analyze a number of...
State-of-the-art Speaker Identification (SI) systems use Gaussian Mixture Models (GMM) for modeling speakerspsila data. Using GMM, a speaker can be identified accurately even from a large number of speakers, when model complexity is large. However, lower ordered speaker model using GMM show poor accuracy as lesser number of Gaussian are involved. In SI context, not much attention have been paid towards...
A state of the art speaker identification (SI) system requires a robust feature extraction unit followed by a speaker modeling scheme for generalized representation of these features. Over the years, mel-frequency cepstral coefficients (MFCC) modeled on the human auditory system have been used as a standard acoustic feature set for SI applications. However, due to the structure of its filter bank,...
A state of the art speaker identification (SI) system requires a robust feature extraction unit followed by a speaker modeling scheme for generalized representation of these features. Over the years, Mel-frequency cepstral coefficients (MFCC) modeled on the human auditory system have been used as a standard acoustic feature set for SI applications. However, due to the structure of its filter bank,...
Speaker identification (SI) system needs an efficient feature extraction process and an appropriate speaker model developed from these features. The work introduces the fusion of log Gabor wavelet (LGW) and maximum a posteriori (MAP) estimator for robust text-independent SI system. The focus of this paper is on the robustness to degradations produced by transmission over a telephone channel. Complete...
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