An Urdu speech corpus for emotion recognition

Author:

Asghar Awais12,Sohaib Sarmad3,Iftikhar Saman45,Shafi Muhammad6,Fatima Kiran7

Affiliation:

1. Sino-Pak Center for Artificial Intelligence, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Haripur, Pakistan

2. Department of Electrical Engineering, University of Engineering and Technology, Taxila, Punjab, Pakistan

3. Department of Electrical and Electronic Engineering, University of Jeddah, Jeddah, Saudi Arabia

4. Faculty of Computer Studies, Arab Open University, Riyadh, Saudi Arabia

5. Department of Computing, School of Electrical Engineering and Computer Science, National University of Science and Technology, Islamabad, Pakistan

6. Faculty of Computing and Information Technology, Sohar University, Sohar, Oman

7. TAFE, New South Wales, Australia

Abstract

Emotion recognition from acoustic signals plays a vital role in the field of audio and speech processing. Speech interfaces offer humans an informal and comfortable means to communicate with machines. Emotion recognition from speech signals has a variety of applications in the area of human computer interaction (HCI) and human behavior analysis. In this work, we develop the first emotional speech database of the Urdu language. We also develop the system to classify five different emotions: sadness, happiness, neutral, disgust, and anger using different machine learning algorithms. The Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coefficient (LPC), energy, spectral flux, spectral centroid, spectral roll-off, and zero-crossing were used as speech descriptors. The classification tests were performed on the emotional speech corpus collected from 20 different subjects. To evaluate the quality of speech emotions, subjective listing tests were conducted. The recognition of correctly classified emotions in the complete Urdu emotional speech corpus was 66.5% with K-nearest neighbors. It was found that the disgust emotion has a lower recognition rate as compared to the other emotions. Removing the disgust emotion significantly improves the performance of the classifier to 76.5%.

Publisher

PeerJ

Subject

General Computer Science

Reference54 articles.

1. Analyzing the impact of prosodic feature (pitch) on learning classifiers for speech emotion corpus;Abbas;International Journal of Information Technology and Computer Science,2015

2. Performance evaluation of learning classifiers for speech emotions corpus using combinations of prosodic features;Abbas;International Journal of Computer Applications,2013

3. Development and analysis of speech emotion corpus using prosodic features for cross linguistics;Ali;International Journal of Scientific and Engineering Research,2013

4. A review of physical and perceptual feature extraction techniques for speech, music and environmental sounds;Alías;Applied Sciences,2016

5. Some commonly used speech feature extraction algorithms;Alim,2018

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3