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Remember me on this computer. Enter the email address you signed up with and we'll email you a reset link. Need an account? Click here to sign up. Download Free PDF. The use of wavelets in speaker feature tracking identification system using neural network WSEAS Transactions on Signal Processing, Download PDF Download Full PDF Package This paper.
A short summary of this paper. The use of wavelets in speaker feature tracking identification system using neural network, system identification phd thesis. Box 1, Amman JORDAN narin yahoo. com haleddaq yahoo. com qawasmi philadelphia. com Abstract— Continuous and Discrete Wavelet Transform WT are used to create text-dependent robust to noise speaker recognition system. In this paper we investigate the accuracy of identification the speaker identity in non- stationary signals.
Three methods are used to extract the essential speaker system identification phd thesis based on Continuous, Discrete Wavelet Transform and Power Spectrum Density PSD.
To have better identification rate, two types of Neural Networks NNT are studied: The first is Feed Forward Back Propagation Neural Network FFBNN and the second is perceptron. Up to The presented system depends on the multi-stage features extracting due to its better accuracy. The multistage features tracking based system shows good capability of features tracking for tested signals with SNR equals to -9 dB using Wavelet Transform, which is suitable for non-stationary signal.
Key-words — Speaker identification; Continuous and discrete wavelet transform; Linear prediction coefficient; and text-dependent. Such utterance []. systems extract features from speech signal, process Over last four decays many solutions of speaker them and use them to recognize the person from the recognition have been appeared in literatures []. Automated speaker Recognition ASR density function of the sample.
All audio techniques systems have immediate advantages in any start by converting the raw speech signal into a application requiring high degree of security as sequence of acoustic feature vectors carrying distinct required in the banking sector and military, among information about the signal.
This feature extraction others. The most speaker recognition typically out-per ASR form their commonly used acoustic vectors are Mel Frequency text independent counter parts due to the more Cepstral Coefficients MFCC [13,14], system identification phd thesis, Linear simple application of the ASR task. Text-dependent Prediction Cepstral Coefficients LPCC [], and refers to the speaker having to say a set utterance for Perceptual Linear Prediction Cepstral PLPC identification, as opposed to text- independent Coefficients.
regression coefficients. A spectral envelope A method using statistical dynamic features has reconstructed from a truncated set of cepstral recently been proposed.
In this method, a coefficients is much smoother than one reconstructed Multivariate Auto-Regression MAR model is from LPC coefficients. Therefore it provides a more applied to the time series of cepstral vectors and used stable representation from one repetition to another to characterize speakers [26].
of a particular speaker's utterances. As for the regression coefficients, typically the first- and second-order coefficients are extracted at every 2 Vocal Tract Model frame period to represent the spectral dynamics. When a person speaks the lungs work like a power These coefficients are derivatives of the time supply of the speech generating system. The glottis functions of the cepstral coefficients and are supplies the input with the certain pitch frequency respectively called the delta- and delta-delta-cepstral F0.
The vocal tract, which contains the pharynx and coefficients. the mouth and nose cavities, works like a musical Text-dependent methods are usually based on instrument to generate a sound.
In fact, different template-matching techniques. In this approach, the vocal tract character or shape would generate a input system identification phd thesis is represented by a sequence of different sound wave. To form distinct vocal tract feature vectors, generally short-term spectral feature shapes, the mouth cavity plays the important role. The time axes of the input utterance and Nasal cavity is often included in the vocal tract each reference template or reference model of the system.
The nasal cavity and the mouth cavity are registered speakers are aligned using a dynamic time connected in parallel. The vocal tract model is shown warping Discrete wavelet Transform DTW in Figure. algorithm and the degree of similarity between them, The glottal pulse produced by the glottis is used to accumulated from the beginning to the end of the generate vowels or sounds.
And the noise-like signal utterance, is calculated. Therefore, HMM-based methods were Hz. accuracies [27]. One of the most successful text-independent recognition methods is based on Vector Quantization VQ. In this method, VQ codebook s consisting of a system identification phd thesis number of representative feature vectors are used as an efficient means of characterizing speaker- specific features.
A speaker-specific codebook is generated by clustering the training feature vectors of each speaker. In the recognition stage, an input utterance is vector-quantized using the codebook of each reference speaker and the VQ distortion accumulated over the entire input utterance is used to make the recognition decision.
Temporal variation in speech signal parameters Figure 1: Vocal tract model over the long term can be represented by stochastic Markovian transitions between states. Therefore, In this research significant in term of using the methods using an ergodic HMM, where all possible Wavelet Transform based recognition system is transitions between states are allowed, have been presented. This system is divided into two main proposed. Speech segments are classified into one of blocks; features extracting and identification.
The the broad phonetic categories corresponding to the advantage of the system is Wavelet transform using. HMM states. After the classification, appropriate The speech signal is given to three stages of features features are selected. This transform that depends on is like a mathematical microscope with properties convolution with wavelet functions, can track the that do not depend on the magnification.
very quick variation in frequency changing. That In Fourier space, we have: what exactly happens in non-stationary signal such as speech signal? conclusion is system identification phd thesis. Now consider a function W a, b which is the wavelet transform of a given function f x, system identification phd thesis.
The Fourier analysis brings only global information which is not enough to detect compact where: patterns. coherence time determined by the geometry of the oral cavity. Morlet has introduced the System identification phd thesis The reconstruction is only available if C χ is system identification phd thesis Transform [21] in order to have a coherence time admissibility condition. In the case proportional to the period.
the mean of the wavelet function is 0. It is a linear transformation, 2, system identification phd thesis. It is covariant under translations: Figure 1 shows these two functions. It Figure 2: Morlet's wavelet: real part at left and imaginary part at right. In system using Matlab. the first block feature extracting is accomplished by Artificial neural networks have advanced in leaps Wavelet Transform and PSD.
But the second block and bounds since their invention in and their presents identification process via verification by first implementation to tackle real world problems NNT. solutions in The first stage of this method is to decompose the The history of neural networks, a general speech signal into Continuous Wavelet Transform description of neural networks, the different types of sub-signals of given scale that must based on speaker architectures and the networks associated with them system identification phd thesis feature frequency depending on its anatomical as well as their applications is presented in many structure of his own vocal tract and other working references.
parts through speaking process Figure. The The latest developments in the research of neural continuous wavelet transform scale determination is networks are providing society with new and very challenging problem because of its non- exciting methods of dealing with complicated stationary nature contained in speech signals; problems and tedious tasks.
It can be concluded that therefore we use experimental scale determination by the future of artificial neural networks and artificial studying huge data base of about speech intelligence looks very promising [28]. This assists greatly in finding out one scale Feed-Forward FF Back propagation Network matching all people, system identification phd thesis. To extract sharp and more that is applied in our research is implemented by concentrated features, we use PSD figure.
Now if using Matlab function new FF, system identification phd thesis, which consists of N these features match any of our models stored in the layers using the dot product weight function, net sum system, system will go on to next stage, if not, net input function, and the specified transfer system will cancel the trail, system identification phd thesis.
To take system identification phd thesis function. decision two NNT are studied: FFBNN and The first layer has weights coming from the input. Each subsequent layer has a weight coming from the The second stage of feature tracking method is previous layer, system identification phd thesis.
All layers have biases, system identification phd thesis. The last layer discrete wavelet transform feature tracking. In system identification phd thesis is the network output.
Each layer's weights and stage the speech signal is decomposed into discrete biases are initialized with Matlab command. This learning functionwhich updates weights with the accomplished by convolution the signal with mother specified learning function. Training is done with the wavelet function to achieve high pass sub-signals of specified training function.
Performance is measured speech signal. And s Jwhich is accomplished by according to the specified performance function. Back-propagation is the most popularly used convolution with father wavelet function scaled method for training multi-layer feed-forward version to achieve low pass sub-signals of the networks.
For most networks, system identification phd thesis, the learning speech signal.
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