Identification of English Dialects and Emotions using Spectral and Prosodic Features of Speech Signal Processing

Authors

  • M. Chinna Rao
  • A. V. S. N. Murthy
  • Ch. Satyanarayana

Abstract

Abstract
In this paper, the authors have explored speech features to identify English dialects and emotions. A dialect is any distinguishable variety of a language spoken by a group of people. Emotions provide naturalness to speech. Speech database considered for dialect identification task consists of spontaneous speech spoken by male and female speakers. The emotions considered in this study are anger, disgust, fear, happy, neutral and sad. Prosodic and spectral features extracted from speech are used for discriminating the dialects and emotions. Spectral features are represented by Mel frequency cepstral coefficients (MFCC) and prosodic features are represented by durations of syllables, pitch and energy contours. Auto-associative neural network (AANN) models and support vector machines (SVM) are explored for capturing the dialect-specific and emotionspecific information from the above specified features. AANN models are expected to capture the nonlinear relations specific to dialects or emotions through the distributions of feature vectors. SVMs perform dialect or emotion classification based on discriminative characteristics present among the dialects or emotions. Classification systems are developed separately for dialect classification and emotion classification.

Keywords: emotion recognition, English dialect, prosodic features, spectral features, support vector machines

Downloads

Published

2019-07-09

Issue

Vol. 4 No. 2 (2013)

Section

Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.