An Urdu speech corpus for emotion recognition

PeerJ Computer Science

Sarmad Sohaib¹  Saman Iftikhar²  Muhammad Shafi³  Awais Asghar⁴  Kiran Fatima⁵ 

[1] Department of Electrical and Electronic Engineering, University of Jeddah, Jeddah, Saudi Arabia;Faculty of Computer Studies, Arab Open University, Riyadh, Saudi Arabia;Faculty of Computing and Information Technology, Sohar University, Sohar, Oman;Sino-Pak Center for Artificial Intelligence, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Haripur, Pakistan;TAFE, New South Wales, Australia;

关键词: Human computer interaction;    Linear prediction coefficient (LPC);    Mel frequency capstrum coefficient (MFCC);    Speech descriptors;    Machine learning algorithms;    Urdu;   

DOI  :  10.7717/peerj-cs.954

来源: DOAJ

【 摘 要 】

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%.

【 授权许可】

Unknown