A Review of Feature Extraction and Classification Techniques in Speech Recognition

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.

Automatic speech recognition (ASR) has gained wide popularity in last decade. Various devices like mobile phones, computers, vehicles, and audio/video players are now being equipped with ASR technology. The increasing use and dependence on ASR technology leads to research enhancements and opportunities in this domain. This chapter provides a detailed review of various advancements in ASR systems development. It highlights history of speech recognition followed by detailed insight into recent advancements and industry leaders providing latest solutions. ASR framework has been discussed in detail which includes feature extraction techniques, acoustic modeling techniques, and language modeling techniques. The chapter also lists various popular data sets available and discusses generation of new data sets. This work will be helpful for the researchers who are new to this field and are exploring development of new speech recognition techniques.

This paper investigates fault diagnosis in batch processes and presents a comparative study of feature extraction and classification techniques applied to a specific biotechnological case study: the fermentation process model by Birol et al. (Comput Chem Eng 26:1553-1565, 2002), which is a benchmark for advanced batch processes monitoring, diagnosis and control. Fault diagnosis is achieved using four approaches on four different process scenarios based on the different levels of noise so as to evaluate their effects on the performance. Each approach combines a feature extraction method, either multi-way principal component analysis (MPCA) or multi-way independent component analysis (MICA), with a classification method, either artificial neural network (ANN) or support vector machines (SVM). The performance obtained by the different approaches is assessed and discussed for a set of simulated faults under different scenarios. One of the faults (a loss in mixing power) could not be detected due to the minimal effect of mixing on the simulated data. The remaining faults could be easily diagnosed and the subsequent discussion provides practical insight into the selection and use of the available techniques to specific applications. Irrespective of the classification algorithm, MPCA renders better results than MICA, hence the diagnosis performance proves to be more sensitive to the selection of the feature extraction technique.

Isolated Speech recognition systems (ISRS) have been implemented using microprocessors, digital signal processors and FPGAs and have been reported in the literature. In this paper, the study and implementation of an ISRS using Cypress Programmable System on Chip (PSoC) is presented. For the implementation, PSoC5 containing the ARM Cortex-M3 CPU is used. Recognition performance is studied using three feature extraction techniques (Zero crossing, Zero crossing with end point detection and Zero crossing with end point detection and Cochlear filter) and minimum distance classifier. The ISRS is trained and tested using 10 utterances of three words. The performance is evaluated for utterances by both single speaker and multiple speaker. From this study, it is found that the system using the Zero crossing with end point detection and Cochlear filter gives the highest recognition accuracy of 80%and 70% respectively for the utterances by single speaker and multiple speakers. Higher recognition accuracy can be obtained using more robust feature extraction and classification techniques at the cost of increase in the computation complexity and storage space. The proposed ISRS has applications in voice operated robots, appliances and access control systems.

Static and dynamic analysis are widely used in malware research. Although both approaches present considerable advantages according to their specific applications, they also present some challenges in terms of the time and resources constraints when extracting features. To alleviate those constraints, recent research works explore the use of techniques such as computer vision, in detecting malicious samples and their corresponding malware family. In this research, we aim to conduct a thorough comparison between a combination set of different feature extraction and classification techniques in order to identify the optimal approach that can be used in a real-time malware detection application portable executable (PE). Multiple Machine Learning (ML) and Deep Learning (DL) algorithms were used with image-based features and non image-based features. The results obtained show high performance when using static PE header features with K-Nearest Neighbor (KNN) and Bagged Trees algorithms for bi-level classification and Image-based Fast Fourier Transform (FFT) features with AlexNet for multi-level classification.

Phoneme recognition is an essential step in the development of a speech recognition system (SRS), as phonemes are fundamental building blocks in a spoken language. This research work aimed to present phoneme recognition with systematic confusion analysis for the Hindi language. The accuracy of phoneme recognition is the foundation for developing an efficient SRS. Therefore, the systematic confusion analysis for phoneme recognition is essential to improve speech recognition performance. Experiments conducted on Continuous Hindi speech corpus for phoneme recognition with speaker-dependent mode using Hidden Markov Model (HMM) based tool kit HTK. Feature extraction technique Perceptual Linear Predictive Coefficient (PLP) was used with five states Monophones HMM model. Tests were performed for exploring the recognition of Hindi vowels and consonants. Confusion matrices were presented for both vowels and consonants with analysis and possible solutions. During systematic analysis, the vowels were divided into front, middle, and back vowels while consonants were categorized based on place of articulation and manner of articulation. Research findings show that some Hindi phonemes have significant effects on speech recognition. The investigations also reveal that some Hindi phonemes are mostly confused, and some phonemes have more deletions and insertions. The research further demonstrates that the words made of less number of phonemes show more insertion errors. It was also found that most of the Hindi sentences end with some specific words. These particular words can be used to reduce the search place in language modeling for improving speech recognition. The research findings can be utilized to enhance the performance of the speech recognition system by selecting suitable feature extraction techniques and classification techniques for phonemes. The outcome of the research can also be used to develop improved pronunciation dictionaries and designing the text for developing phonetically balanced speech corpus for improvement in speech recognition. Experimental results show an average corrected recognition score of 70% for vowel class and consonant categories, the maximum average corrected recognition score of 94% was obtained with palatal sounds, and the lowest average corrected recognition score of 54% was achieved with liquid sounds. The comparative analysis of the presented work was made to similar existing works.

The rapid emergence of technology and big data science opened up a significant amount of work that has been carried out in the field of feature extraction and classification techniques of heart sound using various deep learning methods. Practically, medical practitioners use the same old scientific method and practice to seek out any cardiac disorders and predict any abnormality in the human heart. Heart sound normalization, denoising, segmentation, feature extraction, and classification techniques provide a suitable way of study of phonocardiography (PCG) signal analysis which eventually reduces the cost, makes the system compact, and simultaneously, can work with extensive training data. This paper mainly indulges in two parts feature extraction and classification. The proposed deep learning study for PCG signal used online available heart disease datasets, and time domain features like average energy, power, root mean square (RMS), total harmonic distortion, and zero Crossing rates are used. Statistical features used are kurtosis and skewness. The acoustic features used are Mel-frequency cepstrum coefficients (MFCCs), mel, chroma, contrast, and tonnetz. For the classification of heart sound, the proposed modified CNN-based mobile network is used. The modified CNN-based mobile network is very effective in heart sound analysis as it requires very less computation time and storage. The proposed CNN-based modified Mobile Network model attained an accuracy of 99.04 + 0.07% on the test dataset with a sensitivity of 96.8 + 0.03 % and specificity of 97.2 + 0.09%.

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Lately, 3D imaging techniques have achieved a lot of progress due to recent developments in 3D sensor technologies. This leads to a great interest regarding 3D image feature extraction and classification techniques. As pointed out in literature, one of the most important and discriminative features in images is the textural content. Within this context, we propose a texture feature extraction technique for volumetric images with improved discrimination power. The method could be used in textured volumetric data classification tasks. To achieve this, we fuse two complementary pieces of information, feature vectors derived from Local Binary Patterns (LBP) and the Gray-Level Co-occurrence Matrix-based methods. They provide information regarding the image pattern and the contrast, homogeneity and local anisotropy in the volumetric data, respectively. The performance of the proposed technique was evaluated on a public dataset consisting of volumetric textured images affected by several transformations. The classifiers used are the Support Vector Machine, k-Nearest Neighbours and Random Forest. Our method outperforms other handcrafted 3D or 2D texture feature extraction methods and typical deep-learning networks. The proposed technique improves the discrimination power and achieves promising results even if the number of images per class is relatively small.

Face recognition has become an important topic within the field of pattern recognition and computer vision. In this field a number of different approaches to feature extraction, modeling and classification techniques have been tested. However, many questions concerning the optimal modeling techniques for high performance face recognition are still open. The face recognition system developed by our research group uses a discrete cosine transform (DCT) combined with the use of pseudo-2D hidden Markov models (P2DHMM). In the past our system used continuous probability density functions and was tested on a smaller database. This paper addresses the question of the presence of a major difference in recognition performance with discrete production probabilities compared to continuous ones. Therefore the system is tested using a larger subset of the FERET database. We show that we are able to achieve higher recognition scores and an improvement concerning the computation speed by using discrete modeling techniques.

The implementation of a brain–computer interface (BCI) using electroencephalography typically entails two phases: feature extraction and classification utilizing a classifier. Consequently, there are numerous disordered combinations of feature extraction and classification techniques that apply to each classification target and dataset. In this study, we employed a neural network as a classifier to address the versatility of the system in converting inputs of various forms into outputs of various forms. As a preprocessing step, we utilized a transposed convolution to augment the width of the convolution and the number of output features, which were then classified using a convolutional neural network (CNN). Our implementation of a simple CNN incorporating a transposed convolution in the initial layer allowed us to classify the BCI Competition IV Dataset 2a Motor Imagery Task data. Our findings indicate that our proposed method, which incorporates a two-dimensional CNN with a transposed convolution, outperforms the accuracy achieved without the transposed convolution. Additionally, the accuracy obtained was comparable to conventional optimal preprocessing methods, demonstrating the effectiveness of the transposed convolution as a potential alternative for BCI preprocessing.

Facial expression research on unconstrained spontaneous expressions has benefited from recent advances in feature extraction, dimensionality reduction, and classification techniques. While the facial action coding relies on temporal predictors, state-of-the-art facial expression recognition techniques have been slow to adapt to temporal methods. Further, despite strong evidence of sparsity in the visual cortex, few approaches to facial understanding utilize sparse representations. This paper proposes using a temporal detector based upon facial dynamics, a static expression detector based on sparse representations, and an advanced temporal-sparse fused emotion estimator. Our approach leverages techniques from both computer vision and human brain research to produce a state-of-the-art emotion estimator on unconstrained faces in natural conditions.

Several researches focus on the study and conservation of bees, largely because of its importance for agriculture. However, the identification of bee species has hampering new studies, since it demands a very specialized knowledge and is time demanding. Although there are several methods to accomplish this task, many of them are excessively costly, restricting its applicability. For being accessible, the bee wings have been widely used for the extraction of features, since it is possible to apply morphometric techniques using just one image of the wing. As the manual measurement of various features is tedious and error prone, some systems have been developed for this purpose. However, these systems also have limitations, and there is no study concerning classification techniques that can be used for this purpose. This research aims to evaluate the feature extraction and classification techniques in order to determine the combination of more appropriate techniques for discriminating species of bees. The results of our research indicate that the use of a conjunction of Morphometric and Pixel-based features is more effective than only using Morphometric features. Our analysis also concluded that the best classification algorithms using both only Morphometric features and a conjunction of Morphometric and Pixel-based features are, respectively, Nave Bayes and Logistic classifier. The results of this research can guide the development of new systems to identify bee species in order to assist in researches conducted by biologists.

Mobile technology is very fast growing and incredible, yet there are not much technology development and improvement for Deaf‐mute peoples. Existing mobile applications use sign language as the only option for communication with them. Before our article, no such application (app) that uses the disrupted speech of Deaf‐mutes for the purpose of social connectivity exists in the mobile market. The proposed application, named as vocalizer to mute (V2M), uses automatic speech recognition (ASR) methodology to recognize the speech of Deaf‐mute and convert it into a recognizable form of speech for a normal person. In this work mel frequency cepstral coefficients (MFCC) based features are extracted for each training and testing sample of Deaf‐mute speech. The hidden Markov model toolkit (HTK) is used for the process of speech recognition. The application is also integrated with a 3D avatar for providing visualization support. The avatar is responsible for performing the sign language on behalf of a person with no awareness of Deaf‐mute culture. The prototype application was piloted in social welfare institute for Deaf‐mute children. Participants were 15 children aged between 7 and 13 years. The experimental results show the accuracy of the proposed application as 97.9%. The quantitative and qualitative analysis of results also revealed that face‐to‐face socialization of Deaf‐mute is improved by the intervention of mobile technology. The participants also suggested that the proposed mobile application can act as a voice for them and they can socialize with friends and family by using this app.

The Internet of Things (IoT) with advanced machine learning techniques presents significant potential for agricultural applications, particularly in the domain of crop disease detection. Paddy, a staple food for millions, is highly susceptible to various diseases that can drastically affect yield and quality. Early and accurate disease detection is crucial for effective management and mitigation. Traditional methods are often labor-intensive and less reliable, underscoring the need for automated, accurate, and scalable solutions. The primary challenge lies in developing a robust system capable of accurately identifying diseases in paddy leaves using IoT-collected data. This task is complicated by the variability in disease manifestation and environmental conditions, which can affect the quality and consistency of the collected data. Efficient feature extraction and classification techniques are essential to address these issues and ensure high accuracy. This study proposes a novel approach combining Particle Swarm Optimization (PSO) for feature extraction with Deep Belief Networks (DBNs) for classification. IoT devices capture high-resolution images of paddy leaves, which are then processed in the cloud. PSO is employed to optimize the feature extraction process by selecting the most relevant features from the image data. These optimized features are fed into a DBN, which is trained to classify the images into healthy or diseased categories. The use of cloud computing ensures the scalability and computational efficiency of the system. The proposed method demonstrates significant improvements in accuracy and processing speed. The PSO-based feature extraction enhances the relevance of features, reducing the dimensionality and improving the DBN''s performance. Experimental results show an accuracy rate of 96.3%, with a reduction in processing time by 35% compared to traditional methods. The system''s precision and recall rates are 95.8% and 94.7%, respectively, highlighting its effectiveness in real-world applications.