Emotion recognition using MLP and GMM for Oriya language

Palo, Hemanta Kumar; Chandra, Mahesh; Mohanty, Mihir Narayan

doi:10.1504/ijcvr.2017.084987

Cited by 29 publications

(9 citation statements)

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“…The aim is to classify and compare the accuracy and Mean Square Error performance using two of the mostly used adaptive learning functions such as Gradient Descent (GD) and Gradient Descent with Momentum (GDM). The MLP is uses the back-propagation algorithm while updating weights and biases [11][12]. It contains input, hidden and the output layers as three major units as exposed in Fig.4.…”

Section: Short Time Fourier Transform (Stft)mentioning

confidence: 99%

Detection of Arrhythmia using Neural Network

Mohapatra

Palo

Mohanty

2018

Annals of Computer Science and Information Systems

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Section: Short Time Fourier Transform (Stft)mentioning

confidence: 99%

Detection of Arrhythmia using Neural Network

Mohapatra

Palo

Mohanty

2018

Annals of Computer Science and Information Systems

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“…Some emotions are overlapped and confusing. Few basic emotions as anger, happiness, sadness, boredom, surprise and fear has been found in literatures more often [1][2][3][4][5]. However, emotions as joy, disgust, anxiety and similar nature, emotions out shadow these basic emotions.…”

Section: Introductionmentioning

confidence: 97%

“…Statistics as mean, median, standard deviation, maximum, minimum, range, etc., of these features have been attempted in this field. Features as the fundamental frequency, formants, amplitude, energy, speech rate, Linear Prediction Coefficient (LPC), Mel-Frequency Cepstral Coefficient (MFCC), Perceptual Linear Prediction (PLP) and other spectral variants have been found in literature [1][2][3][4][5][6]. Selection of suitable feature is one of the basic design criteria for emotion detection system.…”

Section: Introductionmentioning

confidence: 99%

“…Classifiers as Artificial Neural Network (ANN), Gaussian Mixture Model (GMM), Hidden Markov Model (HMM), decision tree, KNearest Neighbor (KNN), Maximum-Likelihood Bayes (MLB), Support Vector Machine (SVM), Kernel Regression (KR), Probabilistic Neural Network (PNN) and random forest etc. have been applied in recognizing emotion in speech signals [3][4][5][6][7][8][9][10][11]. Neural network classifiers as MLP, Radial Basis Function Network (RBFN), and PNN have been quite effective in this field [3-5, 9, and 11-12].…”

Section: Introductionmentioning

confidence: 99%

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Reduced feature extraction for emotional speech recognition

Palo

Mohanty

2015

2015 Annual IEEE India Conference (INDICON)

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There has been a considerable use of acoustic features for speaker identification and recognition. Few of these features have also been used by researchers to recognize emotions in speech effectively. Here an attempt is made to characterize human speech emotions with acoustic features as speech rate, formant frequencies, amplitude and energy initially. Further, a reduced acoustic feature set based on difference between consecutive feature values has been proposed. Multilayer Perceptron (MLP) classifier has been put to test for simulation with both baseline and reduced feature sets. The proposed feature sets outperformed baseline features in terms of classification accuracy and Mean Square Error (MSE) as manifested in the results.

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“…Researchers are investigating different heterogeneous sources of features to improve the performance of speech emotion recognition systems. In [24], performances of Mel-frequency cepstral coefficient (MFCC), linear predictive cepstral coefficient (LPCC) and perceptual linear prediction (PLP) features were examined for recognizing speech emotion that achieved a maximum accuracy of 91.75% on an acted corpus with PLP features. This accuracy is relatively low when compared to the recognition accuracy of 95.20% obtained for a fusion of audio features based on a combination of MFCC and pitch for recognizing speech emotion [25].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

Zvarevashe

Olugbara

2020

Algorithms

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Automatic recognition of emotion is important for facilitating seamless interactivity between a human being and intelligent robot towards the full realization of a smart society. The methods of signal processing and machine learning are widely applied to recognize human emotions based on features extracted from facial images, video files or speech signals. However, these features were not able to recognize the fear emotion with the same level of precision as other emotions. The authors propose the agglutination of prosodic and spectral features from a group of carefully selected features to realize hybrid acoustic features for improving the task of emotion recognition. Experiments were performed to test the effectiveness of the proposed features extracted from speech files of two public databases and used to train five popular ensemble learning algorithms. Results show that random decision forest ensemble learning of the proposed hybrid acoustic features is highly effective for speech emotion recognition.

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Emotion recognition using MLP and GMM for Oriya language

Cited by 29 publications

References 0 publications

Detection of Arrhythmia using Neural Network

Detection of Arrhythmia using Neural Network

Reduced feature extraction for emotional speech recognition

Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

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