It was found that the disgust emotion has a lower recognition rate as compared to the other emotions. The recognition of correctly classified emotions in the complete Urdu emotional speech corpus was 66.5% with K-nearest neighbors. To evaluate the quality of speech emotions, subjective listing tests were conducted. The classification tests were performed on the emotional speech corpus collected from 20 different subjects.

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. We also develop the system to classify five different emotions: sadness, happiness, neutral, disgust, and anger using different machine learning algorithms.

In this work, we develop the first emotional speech database of the Urdu language. Emotion recognition from speech signals has a variety of applications in the area of human computer interaction (HCI) and human behavior analysis. Speech interfaces offer humans an informal and comfortable means to communicate with machines. PeerJ Computer Science 8: e954 Įmotion recognition from acoustic signals plays a vital role in the field of audio and speech processing. An Urdu speech corpus for emotion recognition. Cite this article Asghar A, Sohaib S, Iftikhar S, Shafi M, Fatima K. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Licence This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. 7 TAFE, New South Wales, Australia DOI 10.7717/peerj-cs.954 Published Accepted Received Academic Editor Muhammad Asif Subject Areas Human-Computer Interaction, Artificial Intelligence, Data Mining and Machine Learning, Natural Language and Speech, Sentiment Analysis Keywords Human computer interaction, Linear prediction coefficient (LPC), Mel frequency capstrum coefficient (MFCC), Speech descriptors, Machine learning algorithms, Urdu, Emotion recognition, Human behavior analysis Copyright © 2022 Asghar et al.

Urdu speech to text software