نتایج جستجو برای: coefficient mfcc
تعداد نتایج: 170818 فیلتر نتایج به سال:
This paper proposes a new investigation on Gaussian mixture model (GMM) by comparing it with some preliminary experiments on multilayered perceptron network (MLP) with backpropagation learning algorithm (BKP) and dynamic time warping (DTW) techniques on Thai text-dependent speaker identification system. Three major identification engines are conducted on 50 speakers with isolated digits 0-9. Tr...
This paper is to compare two most common features representing a speech word for speech recognition on the basis of accuracy, computation time, complexity and cost. The two features to represent a speech word are the linear predict coding cepstra (LPCC) and the Mel-frequency cepstrum coefficient (MFCC). The MFCC was shown to be more accurate than the LPCC in speech recognition using the dynamic...
The aim of this work is to develop methods that enable acoustic speech features to be predicted from mel-frequency cepstral coefficient (MFCC) vectors as may be encountered in distributed speech recognition architectures. The work begins with a detailed analysis of the multiple correlation between acoustic speech features and MFCC vectors. This confirms the existence of correlation, which is fo...
Speaker recognition is an ability to identify speaker’s characteristics based from spoken language. The purpose of this study gender speakers on audio recordings. objective evaluate the accuracy rate technique differentiate and also determine performance classify even when using self-acquired Audio forensics uses voice recordings as part evidence solve cases. This mainly conducted provide easie...
Abstract Aiming at the issue that recognition accuracy of traditional acoustic signal features is low for helicopter signals with wind noise in near field, a method extracting mixed MFCC+GFCC based on wavelet decomposition proposed. Firstly, three-layer and reconstruction are applied to signals; then, Mel-Frequency Cepstral Coefficients (MFCC) Gammatone-Frequency Cepstrum Coefficient (GFCC) res...
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...
Speaker’s audio is one of the unique identities speaker. Nowadays not only humans but machines can also identify by their audio. Machines different properties human voice and classify speaker from speaker’s Speaker recognition still challenging with degraded limited dataset. be identified effectively when feature extraction more accurate. Mel-Frequency Cepstral Coefficient (MFCC) mostly used me...
An approach to speech feature detection is developed, which uses the technique of independent component analysis for a blind (unsupervised learning) detection of basic vectors in the Fourier space. This kind of features could replace the Mel Frequency Cepstrum Coefficient (MFCC) features, widely used today for phoneme-based speech recognition. Alternatively, the ICA components could act as basi...
In this paper we study the performance of the low-variance multi-taper Mel-frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) features in a state-ofthe-art i-vector speaker verification system. The MFCC and PLP features are usually computed from a Hamming-windowed periodogram spectrum estimate. Such a singletapered spectrum estimate has large variance, which can be red...
The detection of human and spoofed (synthetic/converted) speech has started to receive more attention. In this study, relative phase information extracted from a Fourier spectrum is used to detect human and spoofed speech. Because original/natural phase information is almost entirely lost in spoofed speech using current synthesis/conversion techniques, a modified group delay based feature, the ...
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