Voice Command Intelligent System (VCIS) for Smart Home Application using Mel-frequency cepstral coefficients and linear prediction coefficients

• Main Authors: Abdul Samad, N.A (Author), Abdullah M. (Author), Md Saad, S.Z (Author), Mohd Ali, Y. (Author), Mokhtar, N.F (Author), Noorsal, E. (Author), Zanal, A. (Author), Zulhanip, A.Z (Author)
• Format: Article
• Language: English
• Published: Institute of Physics Publishing, 2020
• Subjects: Automation; Classification (of information); Classification models; Classification rates; Command and control systems; Development process; Domestic appliances; Electrical appliances; Feature extraction algorithms; Intelligent systems; Linear prediction coefficients; Mel frequency cepstral co-efficient; Mel-frequency cepstral coefficients; Neural networks; Semiconductor materials
• Online Access: View Fulltext in Publisher; View in Scopus

 Voice recognition, command and control systems have played a vital role to facilitate our modern lifestyle and routine operations through hands-free applications. Working people are busy with their routines, simple tasks such as to on/off electrical appliances at home are often overlooked leads to wastage of energy. Besides, the elderly and handicapped often face difficulties to control the electrical appliances due to their physical disability. To overcome, this project aims to develop a voice command intelligent system (VCIS) as an aid to these routine tasks such as to switch on/off a lamp, control a motor and control other modes of operation of home appliances using voice interface. In particular, VCIS involves several development processes such as database collection, pre-processing, feature extraction algorithms and classification modeling for application in smart home. This paper proposes three hierarchical stages in VCIS. The experimental results show that Mel-frequency cepstral coefficients (MFCC) surpassed linear prediction coefficients (LPC) to correlate with the voice commands in all classification stages using artificial neural network. The classification rates were successfully accomplished, 99.12% (MFCC) and 95.23% (LPC). In conclusion, the proposed VCIS can be employed as an efficient method for better quality of life. © 2020 IOP Publishing Ltd. All rights reserved.