Linde , Buzo And Gray Research Articles

Speech to Text Conversion System for Myanmar Alphabet

This paper is aimed to implement the speech to text conversion system for Myanmar alphabet. The Myanmar alphabet consists of 33 characters from ?ka? to ?ah?. The proposed system is software architecture which allows the user to speak against the computer in Myanmar language and the corresponding character is printed on the screen in the Microsoft Office Word Document Format. The system is emphasized on Speaker Independent Isolated Word Recognition System. The proposed system directly acquires and converts speech to text. This system contains two main modules: feature extraction and feature matching. Mel Frequency Cepstrum Coefficients (MFCC) is applied for feature extraction which extracts a small amount of data from the voice signal that can later be used to represent each character. Feature matching involves the actual procedure to identify the unknown character by comparing extracted features from the voice inputs of a set of known characters. In this system, Vector Quantization (VQ) approach using Linde, Buzo and Gray (LBG) clustering algorithm, which reduces the amount of data and complexity, is applied for feature matching. To implement this system MATLAB programming language is used.

Real Time Speaker Recognition System using MFCC and Vector Quantization Technique

This paper represents a very strong mathematical algorithm for Automatic Speaker Recognition (ASR) system using MFCC and vector quantization technique in the digital world. MFCC and vector quantization techniques are the most preferable and promising these days so as to support a technological aspect and motivation of the significant progress in the area of voice recognition. Our goal is to develop a real-time speaker recognition system that has been trained for a particular speaker and verifies the speaker. ASR is a type of biometric that uses an individual’s voice for recognition processes. Speaker-vocal discriminative parameters exist in speech signals and due to dissimilar resonances of different speakers speaker recognition system verifies the speaker. These different characteristics can be accomplished by extracting features in vector form like MelFrequency Cepstral Coefficient (MFCCs) from the audio signal. The Vector Quantization (VQ) technique maps vectors from a large vector space to a limited number of regions in the same multidimensional space. LBG (Linde, Buzo and Gray) algorithm is mostly used and preferred for clustering a set of L acoustic vectors into a set of M codebook vectors in speaker recognition.

VHDL Simulation of Image Compression Using LBG

Image-related communications are forming an increasingly large part of modern communications, bringing the need for efficient and effective compression. Image compression is important for effective storage and transmission of images. Image compression is concerned with minimizing the number of bits required to represent an image. The algorithm used for this paper is Linde, Buzo and Gray (LBG) algorithm. It is based on minimization of the squared-error distortion measure. LBG proposed the VQ schemes for gray scale image compression. The basic requirement for this paper is codebook generation. A good codebook is required because the reconstruct image highly depends on the codeword’s in this very codebook. The generated codebook store into text file for VHDL file handling or data array in VHDL code. The VHDL file handling concept used for the quantization. This process of file handling will convert in blockwise conversion in a set of pixel value. The block array splitting by using Pairwise Nearest Neighbor (PNN) principle. The performance of compression ratio will be measure in Mean square Error (MSE) and Peak Signal to Noise Ratio (PSNR). Analysis the synthesis result & timing summary for purpose of design parameter.

Speaker Recognition using Support Vector Machine

recognition is the process of recognizing the speaker based on characteristics such as pitch ,tone in the speech wave.Background noise influences the overall efficiency of speaker recognition system and is still considered as one of the most challenging issue in Speaker Recognition System (SRS). In this paper mel-frequency cepstral coefficients (MFCC) feature is used along with Vector Quantisation(VQ)- LBG (Linde, Buzo and Gray, 1980) algorithm for designing SRS. MFCC feature is extracted from the input speech and then vector quantization of the extracted MFCC features is done using VQLBG algorithm. It reduces the dimensionality of the input vector .These MFCCs are used as the speaker features for matching via Support Vector Machine (SVM) method. The experimental results show that the proposed text- dependent speaker identification system gives an accuracy rate of 95.0%.

Vector Quantized Codebook Optimization Using Modified Genetic Algorithm

Vector Quantization (VQ) is lossy data compression technique and has various applications. Key to VQ is a good codebook. Once the codebook size is fixed, then the mean square error (MSE) reaches a value, beyond which it cannot be reduced by using codebook generation algorithms. In this paper, we are proposing modified genetic algorithm giving the optimal value, but it depends on the initial selection of the codevectors. Hence, it takes extremely huge time to give optimal value. For demonstration, we have used codebooks obtained from Linde Buzo and Gray (LBG) and Kekre’s Fast Codebook Generation (KFCG) algorithms. It is observed that the optimal error obtained from both LBG and KFCG is almost the same, indicating that they have converged to an optimal value. From the results, it is obvious that KFCG codebook gives slightly less minimized error as compared to LBG, indicating that KFCG codebook is closer to the optimum. The proposed method is general and can be applied to any clustering algorithm.

Tumor Demarcation in Mammography Images using LBG on Probability Image

The ability to improve diagnostic information from medical images can be enhanced by designing computer processing algorithms that is why we proposed new algorithm to detect cancer in mammogram breast cancer images. In this paper we proposed segmentation using vector quantization technique. Here we used Linde Buzo and Gray (LBG)for segmentation of mammographic images on probability image. Initially probability of input image is calculated and displayed as a result. In second step a codebook of size 128 was generated for probability image. These code vectors were further reclustered in 8 clusters using same LBG algorithm. These 8 images were displayed as a result. This approach does not leads to over segmentation or under segmentation. For the comparison purpose we displayed results of GLCM and watershed segmentation along with this method.

IMAGE CODING USING HYBRID VECTOR QUANTIZATION

Statistic approach of vector quantization uses code-books of rounded vectors which allow quasi-optimal coding for a given rate. Therefore these code-books have no structure and require big memory size. Regular lattices of points give the possibility of generating a great number of points from a short number of vectors. This can permit us to solve the problems of the statistic approach. However the lattices presents their own problem, effectively they are only applied for uniform distribution sources. In order to avoid these two kinds of problems, another approach is investigated; It consists of designing a new quantizer by combining the two techniques. In this paper, we present this approach which we call hybrid; first few statistic vectors of the source are performed by using the LBG (Linde, Buzo and Gray) algorithm [2], then each of them leads to a Gosset lattice. The results obtained in image coding are also presented.