Phonetic Feature Extraction and Recognition Model in Korean Pronunciation Practice Based on AdaBoost

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.

In this digitally growing era speech emotion recognition plays significant role in several applications such as Human Computer Interface (HCI), lie detection, automotive system to assist steering, intelligent tutoring system, audio mining, security, Telecommunication, Interaction between a human and machine at home, hospitals, shops etc. Speech is a unique human characteristic used as a tool to communicate and express one’s perspective to others. Speech emotion recognition is extracting the emotions of the speaker from his or her speech signal. Feature extraction, Feature selection and classifier are three main stages of the emotion recognition. The main aim of this work is to improve the speech emotion recognition rate of a system using the different feature extraction algorithms. The work emphasizes on the preprocessing of the received audio samples where the noise from speech samples is removed using filters. In next step, the Mel Frequency Cepstral Coefficients (MFCC), Discrete Wavelet Transform (DWT), pitch, energy and Zero crossing rate (ZCR) algorithms are used for extracting the features. In feature selection stage Global feature algorithm is used to remove redundant information from features and to identify the emotions from extracted features machine learning classification algorithms are used. These feature extraction algorithms are validated for universal emotions comprising Anger, Happiness, Sad and Neutral.

For hyperspectral image classification, feature extraction is a crucial pre-process for avoiding the Hughes phenomena. Some feature extraction methods such as linear discriminant analysis (LDA), nonparametric weighted feature extraction (NWFE), and their kernel versions, generalized discriminant analysis (GDA) and kernel nonparametric weighted feature extraction method (KNWFE) have been shown that they can improve the classification performance. However, for GDA and KNWFE, it is hard to find the suitable kernel parameters. Hence, although they have been published about 14 or 6 years, respectively, researchers rarely implement them for dealing with hyperspectral image classification problem. An automatic kernel parameter selection method (APS) was proposed to predetermine the appropriate radial basis function (RBF) kernel for support vector machine (SVM) and GDA. In this study, APS was applied to find the suitable RBF kernel function for KNWFE. From the experiment results on PAVIA data set, the classification performance of KNWFE still outperforms those of GDA [10] and SVM [10]. The most important of this research, the kernel parameters of GDA and KNWFE based on RBF kernel can be “automatically” determined and the researcher can implement them directly without tuning the kernel parameter.

Speech emotion recognition is a trending research topic these days, with its main motive to improve the human-machine interaction. At present, most of the work in this area utilizes extraction of discriminatory features for the purpose of classification of emotions into various categories. Most of the present work involves use of only Mel Frequency Cepstral Coefficients (MFCCs) as an integral feature for emotion recognition. In some other works, the utterance of words is used for lexical analysis for emotion recognition, which is language dependent. In this paper, two different techniques are utilized for classifying emotions into Angry, Happy or Neutral categories. In the first technique, the maximum cross correlation between audio files is computed for labeling the speech data into one of the three emotion categories. Accordingly, a function is developed in MATLAB for Identification of an emotion for any audio file passed as an argument. The second technique makes use of six discriminatory features, namely, Energy, Volume, MFCC, Zero Crossing Rate, Formants and Spectral Centroid. These features are used as predictors for the purpose of classification of emotions. A variety of classifiers are used through the MATLAB classification learner toolbox, and an accuracy of 91.3% is achieved using the Cubic SVM Classifier. The proposed techniques pave way for a real-time prototype for speech emotion recognition in the near future.

The Mel-Frequency Cepstral Coefficients (MFCC) feature extraction method is a leading approach for speech feature extraction and current research aims to identify performance enhancements. One of the recent MFCC implementations is the Delta-Delta MFCC, which improves speaker verification. In this paper, a new MFCC feature extraction method based on distributed Discrete Cosine Transform (DCT-II) is presented. Speaker verification tests are proposed based on three different feature extraction methods including: conventional MFCC, Delta-Delta MFCC and distributed DCT-II based Delta-Delta MFCC with a Gaussian Mixture Model (GMM) classifier.

In the initial stage of learning Japanese, the most common problem is a variety of mistakes in pronunciation. The main reason for these errors is the difference between Chinese and Japanese in pronunciation position and language system. Language learning should combine theory with practice and spend more time on practice with students. Practice has proved that language mastery requires a lot of practical practice, and too much theory may hinder students' flexible mastery of the language. Some media data, such as audio data, can be converted into time series for research. The similarity measurement (pattern matching) of the converted speech time series can find the similar speech signals, which can also be called speech recognition technology. With the rapid development of intelligent control technology, speech signal processing has attracted extensive attention and high attention of researchers.

The unmanned aerial vehicle (UAV) industry is developing rapidly, and the application of UAVs is becoming increasingly widespread. Due to the lowering of the threshold for using UAVs, the random flight of UAVs poses safety hazards. In response to the safety risks associated with the unauthorized operation of UAVs, research on anti-UAV technology has become imperative. This study proposes an improved sound feature extraction method that utilizes the frequency distribution features of UAV sounds. By analyzing the spectrogram of UAV sounds, it was found that the classic Mel Frequency Cepstral Coefficients (MFCC) feature extraction method does not match the frequency bands of UAV sounds. Based on the MFCC feature extraction algorithm framework, an improved frequency band feature extraction method was proposed. This method replaces the Mel filter in the classic algorithm with a piecewise linear function with the frequency band weight as the slope, which can effectively suppress the influence of low- and high-frequency noise and fully focus on the different frequency band feature data of UAV sounds. In this study, the actual flight sounds of UAVs were collected, and the sound feature matrix of UAVs was extracted using the frequency band feature extraction method. The sound features were classified and recognized using a Convolutional Neural Network (CNN). The experimental results show that the frequency band feature extraction method has a better recognition effect compared to the classic MFCC feature extraction method.

A statistically designed Automatic Speech Recognition (ASR) system extracts features from speech signals using feature extraction methods, links the extracted features with the expected phonetics of the hypothesis using acoustic models, uses language model to add prior information about the structure of the target language. For many years, Mel-frequency Cepstral coefficients (MFCC), n-gram, and Hidden Markov Model (HMM) approaches have been used predominantly for feature extraction, language modeling and acoustic modeling, respectively. However, performance degradation of MFCC in noisy conditions and inaccuracy of HMMs while handling large vocabularies have made researchers to propose more efficient methods. The proposed work uses noise robust method Gammatone Frequency Cepstral Coefficients(GFCC) for feature extraction, trigram language modeling, and HMM-Gaussian mixture model (GMM) based acoustic modeling to implement a continuous Hindi language ASR system. Also, it applies Differential Evolution (DE) technique to refine the GFCC features and discriminative techniques to enhance performance of the acoustic model. The performance of the implemented system has been evaluated by using different feature extraction methods, variants of n-gram language modeling techniques and different discriminative techniques in clean as well as noisy conditions. Initially, the results reveal that DE optimized GFCC with HMM-Gaussian Mixture Model (GMM) acoustic modeling performs better than MFCC, PLP and MF-PLP feature extraction methods. Secondly, the experimental results show that the Minimum Phone Error (MPE) outperforms Maximum Mutual Information (MMI) and Maximum Likelihood Estimation (MLE) and trigram based language modeling gives more accurate results than unigram and bigram language modeling. Finally, it has been concluded that the continuous Hindi language ASR system implemented using DE refined GFFC feature extraction method with MPE discriminative training technique and trigram based language modeling gives better accuracy in clean as-well-as noisy environments.

Feature extraction is an important factor to improve the recognition rate of coal and gangue. The existing feature extraction methods have some shortcomings in coal and gangue recognition, such as unsatisfactory recognition rate for actual images. Therefore, aiming at the shortcomings of existing coal and gangue feature extraction methods in coal and gangue recognition, the contour feature extraction method is researched based on images analysis to coal and gangue recognition. The center normalization detrended fluctuation analysis (CNDFA) feature extraction algorithm of contour is proposed based on the process of contour feature extraction for coal and gangue. Based on the analysis of the representative features of coal and gangue, the extraction process of target contour features is established based on hardness difference. Combined with the overall features of contour curve after detrending, a center normalized CNDFA feature extraction algorithm is proposed. First, the detrended analysis of contour curve is realized by least square optimal fitting, and then the detrended data are normalized. Finally, the contour features are described quantitatively by multifractal (MF) spectrum to form the geometric features of the target contour curve, which is used to train the support vector machine classifier. The experiment is carried out on the basis of image preprocessing, and the CNDFA method and other feature extraction methods, such as wavelet, gray level co-occurrence matrix, gray level difference statistics, auto-correlation function, and MF, are applied to the contour feature extraction of coal and gangue. Through the comprehensive comparison of the results after different methods recognition in confusion matrix, accuracy, and coal cleanliness, it is concluded that the overall effect of the CNDFA method is better than other methods, and the accuracy is improved by 5% to 25%. The results show that the CNDFA method has better performance. Compared with the other methods, it can better extract the contour features to improve the recognition rate of coal and gangue.

A human speaker recognition expert often observes the speech spectrogram in multiple different scales for speaker recognition, especially under the short utterance condition. Inspired by this action, this paper proposes a novel multi-resolution time frequency feature (MRTF) extraction method, which is obtained by performing a 2-Dimensional discrete cosine transform (DCT) in multi-scale on the time frequency spectrogram matrix and then selecting and combining to the final multi-scaled transformed elements. Compared to the traditional Mel-Frequency Cepstral Coefficient (MFCC) feature extraction, the proposed method can make better use of multi-resolution temporal-frequency information. Beyond this, we also proposed three complementary combination strategies of MFCC and MRTF: in feature level, in i-vector level and in score level. Comparing their performance. We found the best results are obtained by combination in i-vector level. In the three NIST 2008 Speaker Recognition Evaluation datasets, the proposed method is the most effective for improving the performance under short utterance than under long utterance. And after the combination, we can achieve an EER of 11.32 % and MinDCF of 0.054 in the 10sec-10sec trials on the male dataset, which is an absolute 3 % improvement of EER than the best reported result in this field.

Hearing disabilities affect people all over the world. Nearly 360 million people have a disabling hearing loss. In the recent years, there has been a rapid increase in the number of deaf and dumb victims due to birth defects, accidents and oral diseases. Since deaf and dumb people cannot communicate with a hearing person, they have to depend on some sort of visual communication. Sign Language is not understood by the majority of hearing people. The objective of our project is to form a bridge between the hearing-impaired community and the hearing community; to further commence a two-way communication. We propose a two-way sign language translator that converts Voice to sign language and vice versa in real time. We process the video frame by frame using the OpenCV Library in Python. Furthermore, we use background subtraction method called Gaussian Mixture-based Background/Foreground Segmentation Algorithm on each frame to subtract the background. Contours of this processed image are then passed into a Deep Neural Net (DNN) to classify the frame to the corresponding written language equivalent words. For conversion from sign language to voice, we use basic Natural Language Processing and gtts to accurately conserve the grammar in the sign language. The enrolment rate and literacy among the hearing-impaired children is far below the average for the population at large. This technology will then help the normal schools better integrate the hearing-impaired community thus making education more accessible and cheaper for them. Speech Emotion Recognition (SER) is a vital aspect of human-computer interaction and emotional intelligence applications. This SER that employs Mel- frequency cepstral coefficients (MFCC) as the basis for feature extraction, followed by a Convolutional Neural Network (CNN) with a customized architecture for emotion classification. The methodology 2 involves transforming audio samples into MFCC images, enabling the application of CNNs typically used in computer vision tasks. This fusion of traditional audio analysis with deep learning techniques harnesses the power of MFCC's spectral representation and CNN's spatial pattern recognition. By modifying the CNN architecture, the model can better discern emotional cues in the MFCC images, enhancing SER performance. This strategy offers SER a viable path forward, with the ability to identify complex emotional expressions. In order to increase the accuracy of emotion recognition, it tackles the requirement for feature-rich representations and sophisticated modelling techniques. KEYWORDS: OpenCV, DNN, SER, CNN, MFCC

-- Speech signals are being considered as most effective means of communication between human beings. Many researchers have found different methods or systems to identify emotions from speech signals. Here, the various features of speech are used to classify emotions. Features like pitch, tone, intensity are essential for classification. Large number of the datasets are available for speech emotion recognition. Firstly, the extraction of features from speech emotion is carried out and then another important part is classification of emotions based upon speech. Hence, different classifiers are used to classify emotions such as Happy, Sad, Anger, Surprise, Neutral, etc. Although, there are other approaches based on machine learning algorithms for identifying emotions. Speech Emotion Recognition is a current research topic because of its wide range of applications and it became a challenge in the field of speech processing too. We have carried out a brief study on Speech Emotion Analysis along with Emotion Recognition. Speech Emotion Recognition (SER) can be defined as extraction of the emotional state of the speaker from his or her speech signal. There are few universal emotions including Neutral, Anger, we have worked on different tools to be used in SER. SER is tough because emotions are subjective and annotating audio is challenging task. Emotion recognition is the part of speech recognition which is gaining more popularity and need for it increases enormously. We have classified based on different type of emotions to detect from speech. Key Words: Speech Emotion Recognition, Affective Computing, Machine Learning, Deep Learning, Audio Signal Processing, Emotion Classification, Feature Extraction, Prosodic Features, Spectral Features, Mel-Frequency Cepstral Coefficients (MFCCs), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Attention Mechanisms, Multimodal Emotion Recognition, Speaker-Independent SER, Real-Time Emotion Detection, Noise-Robust Emotion Recognition, Data Augmentation, Emotion-Aware Applications

Brain–computer interface (BCI) is an active domain which has attracted attention of the research community in recent years. It offers huge potential as a technology which can estimate the intention of a user by analysis of brain signals and establish a communication channel directly between a human brain and an external device. Electroencephalography (EEG) is the most popular signal acquisition technique due to its ease of use and simplicity. In EEG-based BCI systems, electrodes are placed on specific positions on the scalp of the subject to record electrical activity. The BCI system consists of sequential stages of signal acquisition, its preprocessing, feature extraction and feature classification. It is an active research area which has a focus on improving classification accuracy in motor imagery-based BCI systems. The first stage in a BCI system is to acquire EEG signals from different positions of the scalp of the human subject. The acquired brain signals are preprocessed to remove artifacts before these are fed to feature the extraction stage. In this paper, independent component analysis (ICA) technique is used to remove artifacts from acquired signals. Filter bank common spatial pattern (FBCSP) technique is then used for feature extraction and feature selection. A feature classification approach based on support vector machine (SVM) is proposed in this work and its performance is enhanced by optimizing its polynomial kernel parameters. Selection of kernel parameters is done by grid search method using the fivefold cross-validation procedure. The proposed approach is then executed on publicly available data set 2a of BCI Competition IV. Results show that the proposed approach offers higher classification accuracy and lower misclassification rate as compared to other methods executed on the same dataset, as reported in literature.

Investigating new features for human cognitive state classification is an intiguing area of research with Electroencephalography (EEG) based signal analysis. We plan to develop a cost-effective system for cognitive state classification using ambulatory EEG signals. A novel event driven environment is created using external stimuli for capturing EEG data using a 14-channel Emotiv neuro-headset. A new feature extraction method, Gammatone Cepstrum Coefficients (GTCC) is introduced for ambulatory EEG signal analysis. The efficacy of this technique is compared with other feature extraction methods such as Discrete Wavelet Transformation (DWT) and Mel-Frequency Cepstral Coefficients (MFCC) using statistical metrics such as Fisher Discriminant Ratio (FDR) and Logistic Regression (LR). We obtain higher values for GTCC features, demonstrating its discriminative power during classification. A superior performance is achieved for the EEG dataset with a novel ensemble feature space comprising of GTCC and MFCC. Furthermore, the ensemble feature sets are passed through a proposed 1D Convolution Neural Networks (CNN) model to extract novel features. Various classification models like Probabilistic neural network (P-NN), Linear Discriminant Analysis (LDA), Multi-Class Support Vector Machine (MCSVM), Decision Tree (DT), Random Forest (RF) and Deep Convolutional Generative Adversarial Network (DCGAN) are employed to observe best accuracy on extracted features. The proposed GTCC, (GTCC+MFCC) & (GTCC +MFCC +CNN) features outperform the state-of-the-art techniques for all cases in our work. With GTCC+MFCC feature space and GTCC+MFCC+CNN features, accuracies of 96.42% and 96.14% are attained with the DCGAN classifier. Higher classification accuracies of the proposed system makes it a cynosure in the field of cognitive science.

Identifying a person's identity still often uses an ID card (KTP, SIM, passport, etc.). This method has a weakness because the ID Card is easily damaged and lost. Biometric recognition systems provide a solution by using human body parts as identity recognition. Sounds are readily available biometric information. Voice pattern recognition is used for the speaker identification process to obtain the identity of someone speaking. This paper reviews several feature extraction and classification methods that are often used in speaker identification. The selection of feature extraction methods and classification functions in computation and the level of accuracy of the speaker identification system. Based on the survey dataset applied with the feature extraction method, the Mel Frequency Cepstral Coefficients (MFCC) method has high accuracy even with noise input. Then in classification, the Gaussian Mixture Model (GMM) method is most often used because it can work in noise. Recently, a hybrid classifier has been developed, which increases the accuracy value.