Feature Extraction Methods Based on Linear Predictive Coding and Wavelet Packet Decomposition for Recognizing Spoken Words in Malayalam

In recent scenario speaker recognition is used in many places for security purpose. Speaker recognition is technique which can verify or identify the person who is speaking. It is different than speech recognition system. In this paper we have discussed the feature extraction techniques like Mel Frequency Cepstral Co-efficient (MFCC), Linear Predictive Coding (LPC) etc. and classification techniques like Gaussian Mixture Model (GMM), Artificial Neural Networks (ANN) etc. that is available for speaker recognition. Also the survey is about how to get better efficiency in terms of speaker recognition rate by simply modifying the existing feature extraction and classification techniques.

Speech recognition is gaining an increasing research interest in the last five decades. Speech processing is considered as an interdisciplinary branch of electronics and computer science domain. It considers speech as an input and converts it into the corresponding text. This paper describes the design and development of Marathi Numeric Speech Dataset. Marathi Numbers (Ank) ranging from Shunya(0) to Nau(9) and are taken into consideration for recording. Speech samples are collected from 50 native and 50 non-native speakers of Marathi language. The dataset remains as a gender balanced since it is recorded from 50 females and 50 male speakers. The age of speakers will affect the speech. Therefore, 5 different age groups such as 11-20, 21-30, 31-40, 41-50 and 51-60 are considered. Native and non-native speakers are selected to obtain ample amount of variations in the pronunciation of Marathi numerals. Feature extraction and feature matching technique plays a vital role for speech recognition and here LPC (Linear Predictive Coding) is used for extracting features from samples, whereas ANN (Artificial neural network) is used to classify them. Experimental specifications and results are also discussed. This research work has attempted to design and develop a speech recognition system, which can understand Marathi Ank (Numbers) and identify them accurately.

The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. Wang B, Yi X, Gao J, etal. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.

Chapter 10 - Parameterization techniques for automatic speech recognition system

In the field of pattern recognition, off-line handwriting recognition is one of the most intensive areas of study. This paper proposes an automatic off-line Thai language student name identification system which was built as a part of a completed off-line automated assessment system. There is limited work undertaken in developing off-line automatic assessment systems using handwriting recognition. To the authors' knowledge, none of the work on the proposed system has been performed on the Thai language. In addition the proposed system recognises each Thai name by using an approach for whole word recognition, which is different from the work found in the literature as most perform character-based recognition. In this proposed system, the Gaussian Grid Feature (GGF) and the Modified Direction Feature (MDF) extraction techniques are investigated on upper and lower contours, loops from full word contour images of each name sample, and artificial neural networks and support vector machine are used as classifiers. The encouraging recognition rates for both feature extraction techniques were achieved when applied on loop, upper and lower contour images (99.27% accuracy rate was achieved using MDF on artificial neural networks and 99.27% using GGF with a support vector machine classifier).

Speech recognition can be defined as the process of converting voice signals into the ranks of the word, by applying a specific algorithm that is implemented in a computer program. The research of speech recognition in Indonesia is relatively limited. This paper has studied methods of feature extraction which is the best among the Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficients (MFCC) for speech recognition in Indonesian language. This is important because the method can produce a high accuracy for a particular language does not necessarily produce the same accuracy for other languages, considering every language has different characteristics. Thus this research hopefully can help further accelerate the use of automatic speech recognition for Indonesian language. There are two main processes in speech recognition, feature extraction and recognition. The method used for comparison feature extraction in this study is the LPC and MFCC, while the method of recognition using Hidden Markov Model (HMM). The test results showed that the LPC method is better than MFCC in Indonesian language speech recognition.

An artificial neural network (ANN) is applied to perform the task of acoustic-to-articulatory inversion. The objective is to model the highly nonlinear mapping from linear predictive coding (LPC) code to corresponding articulatory parameters with a multilayer perceptron ANN structure. Such information will facilitate the study of the relationships between the acoustic signal and the physical vocal tract which produces it. Several novel approaches for devising the ANN structure have been evaluated. Specifically, the performance of two learning algorithms, a backpropagation (BP) algorithm, and a random optimization (RM) algorithms, are compared. To reduce excessive, redundant hidden units in the multilayer perceptron model, a singular value decomposition is applied to either the weight matrix or the output covariance matrix of the hidden units to check their corresponding ranks. In both cases, their ranks are closely related to the number of essential decision regions in the input data. >

This paper describes about speech recognition implemented on ATmega162 microcontroller. The word (voice command) in a speech signal is applied for controlling movement of a mobile robot. The mobile robot will move according to the voice command in the speech signal. There are five commands, which are Indonesian language, used to control the movement of mobile robot. They are “maju”, “mundur”, “kiri”, “kanan”, and “stop” used to command the mobile robot to move forward, move backward, turn left, turn right and stop respectively.The methods used in this research are Linear Predictive Coding (LPC) and Hidden Markov Model (HMM). LPC is used to extract word data from a speech signal. HMM is used to recognize the word pattern data, which are extracted from a speech signal. Sampling rate of the speech signal is 8 kHz and the speech signal is sampled for 0.5 seconds. Experiments were done in several variations of observation symbol number and number of sample. The experimental results show that the highest recognition rate that can be achieved by the system is 87%. The mobile robot can move in accordance with the voice command that is given to the robot.

Speaker recognition is a biometric technique which uses individual speakers voice samples as a input for recognition purpose. Main goal of this work is to obtain better accuracy for speaker recognition system for large number of voice database. In this paper, a comparative study is made between various combinations of features for speaker identification system with feedforward artificial neural network(FFANN) and support vector machine for 10 and 20 speakers. Linear predictive coding, Mel frequency cepstral coefficient and zero crossing rate are used as a feature extraction techniques. Each features are tested separately and in combination with FFANN and SVM classifier on Matlab software. For ANN classifier 70% of total database are used for training,15% for validation and remaining 15% data are used for testing, number of hidden layers and number of neurons used are 2 and 80 respectively. It is observed from the result that efficiency of the system is not dropping by increasing number of speakers from 10(320 voice samples) to 20(640 voice samples) when using combination of LPC, MFCC, ZCR features with ANN classifier.

There is an important military significance about aircrafts type recognition based on shortwave communication. Acoustic signals received by shortwave radio about aircrafts are complex and they contain some valuable information. It is very difficult to identify the aircraft type based on background sound signals of shortwave communication. In order to solve this problem, in this paper two methods for aircrafts type recognition based on shortwave communication are presented. The acoustic signals characteristics of aircrafts are extracted by wavelet packet decomposition (WPD). Support vector machine (SVM) and back propagation (BP) artificial neural network (ANN) as classifiers are adopted to identify five kinds of aircrafts respectively. From results of experiments about classification and recognition of five kinds of aircrafts, the combination of WPD as feature extraction method and SVM as the classifier has better performance and recognition rate than the combination of WPD and BP ANN.

Growth in Digital signal processing technology gives positives influences in human life. One of the branch of science that gives significant influence is digital speech processing. It can be expand into some applications that make human life easier. Digital speech processing is appropriate to speech recognition. Speech recognition is used to arrange the movement of remote control car. The remote control car will move according to our speech. This final project is closely related with speech recognition. The LPC (Linear Predictive Coding) method will extract the speech signal features and HMM (Hidden Markov Model) to modelling the speech signal are used. It is done by comparing model from extracting feature that is available in HMM modelling. Models will be used in the speech recognition process, if the models have highest level of conformity. The experiment has been done in two conditions, i.e ideal condition in room wih low noise level and unideal condition in room with noise. Result of the experiment from the whole sistem performace at ideal condition is 97,71% for people that have been inputed in the database, and 95,42% for people which have not been inputed in the database. Result of the experiment of database say it regulary is 97,14%. Result of the experiment of database at unideal condition is 54,28% for inputing word at noisy area, result of the experiment of database for inputing word at high frequency from siren voice is 98,57% and result of the experiment of inputing similar word with database is 97,6% Keywords: speech recognition, LPC, HMM

Linear predictive coding (LPC) parameters are widely used in various speech coding applications for representing the short-time spectral envelope information of speech [1]. For low bit rate speech coding applications, it is important to quantize these parameters using as few bits as possible. Considerable workhas been done in the past to develop both scalar and vector quantization procedures to quantize the LPC parameters [2, 3,4]. Scalar quantizers quantize each of the LPC parameters independently, while vector quantizers consider the entire set of LPC parameters as an entity and allow for direct minimization of quantization distortion. Because of this, the vector quantizers result in smaller distortion than the scalar quantizers at any given bit rate. The vector quantizers, however, have one major problem; their computational complexity is high. In our earlier paper [3], we have reported on a vector quantizer where the LPC parameter vector is split in the line spectral frequency (LSF) domain to overcome this complexity problem. We have shown that this quantizer can quantize the LPC parameters at 24 bits/frame with an average spectral distortion of 1 dB, less than 2% frames having spectral distortionl in the range 2-4 dB and no frame having spectral distortion greater than 4 dB.

Deep Learning Artificial Neural Networks has pushed forward researches in the field of pattern recognition, furthermore in human handwriting recognition. From online to offline approach, signature verification, writing or writer identification, segmentation or features extraction, a multitude of Artificial Neural Networks (ANNs) models are applied in the process. This paper focuses on the literature review of human handwriting recognition with ANN's over the last decade. We propose an exploratory analysis of 294 research papers collected from five indexed research engines: ACM Digital Library, IEEE digital library, Science Direct, Scopus and Web of Science. Our aim is to provide a research papers distribution across years and journals, a Keywords frequency analysis using cloud visualization, and a Natural Language Processing Topic Modeling using Non-Negative Matrix Factorization (NMF). The results of this study show that the number of research papers reached noticeably a peak in the 2010 with 44 published papers; also Pattern Recognition was the top publishing journal with 12 published papers. As for the topic modeling using NMF we obtained 3 topics listed as follows: 1) Feature Extraction and segmentation techniques for Handwritten Texts Recognition; 2) Signature Verification in Biometric security for Off-line Authentication; 3) Assessment Systems for Student Identification

River Stage Forecasting Using Wavelet Packet Decomposition and Data-driven Models

This research aims to recognize the pattern of pulmonary disease on x-ray radiography image using artificial neural network (ANN) method. The images, which were used such as images of healthy pulmonary, pulmonary tuberculosis, and pulmonary tumour. Pattern recognition was using an extraction feature of GLCM (Gray Level Co-occurrence Matrix) and back propagation method. Before being identified, the images were processed by median filter and adaptive histogram equalization. The GLCM features that used were homogeneity, energy, contrast, variance and correlation. The parameters were learning rate and hidden layer. Learning rate was 0.3 and hidden layer was 25. Back propagation training showed 100% accuracy, which all of 44 images were used had been successfully identified. From the result, the healthy pulmonary showed 60% accuracy, 83.3% for pulmonary tuberculosis and 100% for pulmonary tumor. Hence, the overall result showed 81.25% accuracy, which 13 of 16 images had been successfully identified. From these result, extraction feature of GLCM using back propagation method was capable to recognize the pattern of pulmonary disease. However, due to narrow range of the feature, this application may not be used optimally for comparing features in every category of images. Therefore, the further research is needed to determine the best features and parameters of training back propagation.