Emotion recognition in human-computer interaction

Image analysis and machine learning for detecting malaria

Emotion recognition from speech signals is an important but challenging component of Human-Computer Interaction (HCI). In the literature of speech emotion recognition (SER), many techniques have been utilized to extract emotions from signals, including many well-established speech analysis and classification techniques. Deep Learning techniques have been recently proposed as an alternative to traditional techniques in SER. This paper presents an overview of Deep Learning techniques and discusses some recent literature where these methods are utilized for speech-based emotion recognition. The review covers databases used, emotions extracted, contributions made toward speech emotion recognition and limitations related to it.

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Speech Emotion Recognition Using Deep Learning Techniques: A Review

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Bioelectrical Signal Processing In Cardiac And Neurological Applications

In recent years, popularity of unmanned air vehicles enormously increased due to their autonomous moving capability and applications in various domains. This also results in some serious security threats, that needs proper investigation and timely detection of the amateur drones (ADr) to protect the security sensitive institutions. In this paper, we propose the novel machine learning (ML) framework for detection and classification of ADr sounds out of the various sounds like bird, airplanes, and thunderstorm in the noisy environment. To extract the necessary features from ADr sound, Mel frequency cepstral coefficients (MFCC), and linear predictive cepstral coefficients (LPCC) feature extraction techniques are implemented. After feature extraction, support vector machines (SVM) with various kernels are adopted to accurately classify these sounds. The experimental results verify that SVM cubic kernel with MFCC outperform LPCC method by achieving around 96.7% accuracy for ADr detection. Moreover, the results verified that the proposed ML scheme has more than 17% detection accuracy, compared with correlation-based drone sound detection scheme that ignores ML prediction.

Machine Learning Inspired Sound-Based Amateur Drone Detection for Public Safety Applications

Malaria is responsible for nearly 438,000 deaths worldwide in a year. A total of 214 million cases of malaria are encountered annually. The conventional method for testing malaria is through microscopy. A blood sample of the patient is spread over a glass slide, stained with Giemsa stain and examined under a microscope. It takes a few hours and a highly trained professional to visually examine the slide and give the results. It is even more difficult to detect the different types of malaria parasite and their stages by the conventional methods. The proposed method involves acquisition of the thin blood smear microscopic image at 100x magnification, pre-processing by partial contrast stretching, separation of infected cell from the image by applying k-means clustering on the a∗b component of L∗a∗b color space, feature extraction (shape and textural) of the infected cell, feature reduction using one way ANOVA and finally training the K-nearest neighbor classifier to test the images. Instead of extracting features for the entire group of erythrocytes present in the image, the algorithm only processes the infected cells increasing the speed, effectiveness and efficiency of testing. The KNN classifier is trained with 300 images to detect three lifecycle stages (trophozite, schizont and gametocyte) for each of the four species of malarial parasites (P.falciparum, P.vivax, P.malariae, and P.ovale) with an accuracy of 90.17% and sensitivity of 90.23%.

Detection of malaria parasite species and life cycle stages using microscopic images of thin blood smear

About 3.2 million people suffer from sickle-cell disease. Aim of this paper is to detect sickle cell anaemia and thalassaemia. The proposed method involves acquisition of the thin blood smear microscopic images, pre-processing by applying median filter, segmentation of overlapping erythrocytes using marker-controlled watershed segmentation, applying morphological operations to enhance the image, extraction of features such as metric value, aspect ratio, radial signature and its variance, and finally training the K-nearest neighbor classifier to test the images. The algorithm processes the infected cells increasing the speed, effectiveness and efficiency of training and testing. The K-Nearest Neighbour classifier is trained with 100 images to detect three different types of distorted erythrocytes namely sickle cells, dacrocytes and elliptocytes responsible for sickle cell anaemia and thalassemia with an accuracy of 80.6% and sensitivity of 87.6%.

Detection of sickle cell anaemia and thalassaemia causing abnormalities in thin smear of human blood sample using image processing

To diagnose and classify the dysarthric speech, speech language pathologist (SLP) conducts a listening test. On the basis of the scores given by listeners the dysarthria is diagnosed and assessed. The above mentioned method is costly, time consuming and not very accurate. Unlike the traditional method, this research proposes an automatic diagnosis and assessment of dysarthria. The aim of this paper is to diagnose and classify the severity of dysarthria. The speech disorder specific prosodic features are selected by using genetic algorithm. The diagnosis and assessment of dysarthric speech is done by support vector machines. During diagnosis the classification accuracy of 98% has been achieved. And 87% of the dysarthric speech utterances are correctly classified. The standard UASPEECH database has been used in this work.

An automatic diagnosis and assessment of dysarthric speech using speech disorder specific prosodic features

Automatic identification of bird species based on the chirping sounds of birds was experimented using feature extraction method and classification based on support vector machines (SVMs). The proposed technique followed the extraction of cepstral features on mel scale of each audio recording from the collected standard database. Extracted mel frequency cepstral coefficients (MFCCs) formed a feature matrix. This feature matrix was then trained and tested for efficient recognition of audio events from audio test signals. 70% of the whole database was used for training purpose while the reamaining 30% for testing of samples. The classifier achieved upto 89.4% accuracy on a data set containing four species, commonly found in India.

An automatic classification of bird species using audio feature extraction and support vector machines

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.

Garima Vyas

Papers

Detection of malaria parasite species and life cycle stages using microscopic images of thin blood smear

Detection of sickle cell anaemia and thalassaemia causing abnormalities in thin smear of human blood sample using image processing

An automatic diagnosis and assessment of dysarthric speech using speech disorder specific prosodic features

An automatic classification of bird species using audio feature extraction and support vector machines

Speech Emotion Recognition Using Cross-Correlation and Acoustic Features