New features for emotional speech recognition

Bio-medical research extends towards human voice and auditory systems day by day. Similarly it helps for the security issues. Emotion analysis and recognition for such purpose is a challenging task. To analyze and recognize, the emotions has been attempted in this piece of work. Initially, Sub-band spectral features have been extracted to characterize high arousal angry, happy, fear, surprise and neutral speech emotions. Further, two new features as spectrum and cepstrum with vector quantization has been used. Finally, simulations for robust feature have been applied to Probabilistic Neural Network (PNN) classifier for recognition and the performance. Classifier performance degrades in presence of large unknown data due to data dependency smoothing parameter over sensitization of training data. As Vector Quantization (VQ) has the ability to reduce the feature size, the modified feature sets are developed to improve the classifier robustness. Promising results have been manifested in result section.

[1]  Constantine Kotropoulos,et al.  Emotional speech recognition: Resources, features, and methods , 2006, Speech Commun..

[2]  Ze-Jing Chuang,et al.  Emotion Recognition Using IG-based Feature Compensation and Continuous Support Vector Machines , 2006 .

[3]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[4]  Divakar Yadav,et al.  UNDERSTANDING AND ESTIMATION OF EMOTIONAL EXPRESSION USING ACOUSTIC ANALYSIS OF NATURAL SPEECH , 2013 .

[5]  Mihir Narayan Mohanty,et al.  Emotion recognition using MLP and GMM for Oriya language , 2017, Int. J. Comput. Vis. Robotics.

[6]  Yi-Ping Phoebe Chen,et al.  Acoustic Features Extraction for Emotion Recognition , 2007, 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007).

[7]  Mahesh Chandra,et al.  Use of Different Features for Emotion Recognition Using MLP Network , 2015 .

[8]  Tiago H. Falk,et al.  Automatic speech emotion recognition using modulation spectral features , 2011, Speech Commun..

[9]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[10]  Kuldip K. Paliwal,et al.  Spectral subband centroid features for speech recognition , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[11]  Mihir Narayan Mohanty,et al.  On the Use of MFCC Feature Vector Clustering for Efficient Text Dependent Speaker Recognition , 2013, FICTA.

[12]  Hania Farag,et al.  Emotion Recognition Using Neural Network: A Comparative Study , 2013 .

[13]  Mahesh Chandra,et al.  Design of Neural Network Model for Emotional Speech Recognition , 2015 .

[14]  Mihir Narayan Mohanty,et al.  Classification of Emotional Speech of Children Using Probabilistic Neural Network , 2015 .

[15]  J. G. Taylor,et al.  Emotion recognition in human-computer interaction , 2005, Neural Networks.

[16]  George N. Votsis,et al.  Emotion recognition in human-computer interaction , 2001, IEEE Signal Process. Mag..

[17]  FragopanagosN.,et al.  2005 Special Issue , 2005 .

[18]  Thomas Quatieri,et al.  Discrete-Time Speech Signal Processing: Principles and Practice , 2001 .