Design of Neural Network Model for Emotional Speech Recognition

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

doi:10.1007/978-81-322-2135-7_32

Cited by 10 publications

(2 citation statements)

References 15 publications

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“…In this case, the Gaussian kernel as activation function is used and the distance is evaluated [15]. The hidden layer depends on a non-linear RBF activation function [16][17]. The output of the network is found as the distance between the input vector and the vector of the centre of the Gaussian function and can be expressed as [18][19][20].…”

Section: Radial Basis Function Network (Rbfn) Model For Detectionmentioning

confidence: 99%

A Machine Learning Approach for Speech Detection in Modern Wireless Communication Environment

Dash

Mohanty

2018

IJMLNCE

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Modern wireless communication has gained a improved position as compared to previous time. Similarly, speech communication is the major focus area of research in respective applications. Many developments are done in this field. In this work, we have chosen the OFDM modulation based communication system, as it has importance in both licensed and unlicensed wireless communication platform. The voice signal is passed though the proposed model to obtain at the receiver end. Due to different circumstances, the signal may be corrupted partially at the user end. Authors try to achieve a better signal for reception using a neural network model of RBFN. The parameters are chosen for the RBFN model, as energy, ZCR, ACF, and fundamental frequency of the speech signal. In one part these parameters have eligibility to eliminate noise partially, where as in other part the RBFN model with these parameters proves its efficacy for both noisy speech signals with noisy channel as Gaussian channel. The efficiency of OFDM model is verified in terms of symbol error rate and the transmitted speech signal is evaluated in term of SNR that shows the reduction of noise. For visual inspection, a sample of signal, noisy signal and received signal is also shown. The experiment is performed with 5dB, 10dB, 15dB noise levels. The result proves the performance of RBFN model as the filter.The performance is measured as the listener’s voice in each condition. The results show that, at the time of the voice in noise environment, proposed technique improves the intelligibility on speech quality.

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Section: Radial Basis Function Network (Rbfn) Model For Detectionmentioning

confidence: 99%

A Machine Learning Approach for Speech Detection in Modern Wireless Communication Environment

Dash

Mohanty

2018

IJMLNCE

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“…It has been used in most of the cases. Variouse features such as Linear predictor coefficients (LPC), Linear predictor cepstral coefficients (LPCC), Mel frequency cepstral coefficients (MFCC), Perceptual linear prediction (PLP), LP_VQC and pH vectors are used for emotional speech recognition are found in literature [9][10][11][12]. Similarly, classifiers like Multilayer perceptron (MLP), Radial [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Classification of Emotional Speech of Children Using Probabilistic Neural Network

Palo

Mohanty

2015

IJECE

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<p>Child emotions are highly flexible and overlapping. The recognition is a difficult task when single emotion conveys multiple informations. We analyze the relevance and importance of these features and use that information to design classifier architecture. Designing of a system for recognition of children emotions with reasonable accuracy is still a challenge specifically with reduced feature set. In this paper, Probabilistic neural network (PNN) has been designed for such task of classification. PNN has faster training ability with continuous class probability density functions. It provides better classification even with reduced feature set. LP_VQC and pH vectors are used as the features for the classifier. It has been attempted to design the PNN classifier with these features. Various emotions like angry, bore, sad and happy have been considered for this piece of work. All these emotions have been collected from children in three different languages as English, Hindi, and Odia. Result shows remarkable classification accuracy for these classes of emotions. It has been verified in standard databse EMO-DB to validate the result.</p>

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