Open Access

ARTICLE

Multilayer Neural Network Based Speech Emotion Recognition for Smart Assistance

Sandeep Kumar¹, MohdAnul Haq², Arpit Jain³, C. Andy Jason⁴, Nageswara Rao Moparthi¹, Nitin Mittal⁵, Zamil S. Alzamil^2,*

1 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, 522502, India
2 Department of Computer Science, College of Computer and Information Sciences, Majmaah University, 11952, Al-Majmaah, Saudi Arabia
3 Department of Computer Science and Engineering, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, 244001, India
4 Department of Electronics and Communication Engineering, Sreyas Institute of Engineering and Technology, Hyderabad, 500068, India
5 University Centre for Research and Development, Chandigarh University, Mohali, 140413, Punjab, India

* Corresponding Author: Zamil S. Alzamil. Email:

Computers, Materials & Continua 2023, 74(1), 1523-1540. https://doi.org/10.32604/cmc.2023.028631

Received 14 February 2022; Accepted 24 May 2022; Issue published 22 September 2022

Abstract

Day by day, biometric-based systems play a vital role in our daily lives. This paper proposed an intelligent assistant intended to identify emotions via voice message. A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert actions. This proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes (LED) alert signals and it also keep track of the places like households, hospitals and remote areas, etc. The proposed approach is able to detect seven emotions: worry, surprise, neutral, sadness, happiness, hate and love. The key elements for the implementation of speech emotion recognition are voice processing, and once the emotion is recognized, the machine interface automatically detects the actions by buzzer and LED. The proposed system is trained and tested on various benchmark datasets, i.e., Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) database, Acoustic-Phonetic Continuous Speech Corpus (TIMIT) database, Emotional Speech database (Emo-DB) database and evaluated based on various parameters, i.e., accuracy, error rate, and time. While comparing with existing technologies, the proposed algorithm gave a better error rate and less time. Error rate and time is decreased by 19.79%, 5.13 s. for the RAVDEES dataset, 15.77%, 0.01 s for the Emo-DB dataset and 14.88%, 3.62 for the TIMIT database. The proposed model shows better accuracy of 81.02% for the RAVDEES dataset, 84.23% for the TIMIT dataset and 85.12% for the Emo-DB dataset compared to Gaussian Mixture Modeling(GMM) and Support Vector Machine (SVM) Model.

---

Keywords

---