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Multilayer Neural Network Based Speech Emotion Recognition for Smart Assistance 被引量:2
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作者 Sandeep Kumar MohdAnul Haq +4 位作者 Arpit Jain C.Andy Jason Nageswara Rao Moparthi Nitin Mittal Zamil S.Alzamil 《Computers, Materials & Continua》 SCIE EI 2023年第1期1523-1540,共18页
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 huma... 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. 展开更多
关键词 Speech emotion recognition classifier implementation feature extraction and selection smart assistance
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TelG Mote: A Green Wireless Sensor Node Platform for Smart Home and Ambient Assisted Living 被引量:1
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作者 Mohd Rozaini Bin Abd Rahim Rozeha A.Rashid +2 位作者 Norsheila Fisal Zubair Khalid Abdul Hadi Fikri Abd Hamid 《Journal of Electronic Science and Technology》 CAS CSCD 2016年第3期211-219,共9页
The wireless sensor network (WSN) consists of sensor nodes that interact with each other to collectively monitor environmental or physical conditions at different locations for the intended users. One of its potenti... The wireless sensor network (WSN) consists of sensor nodes that interact with each other to collectively monitor environmental or physical conditions at different locations for the intended users. One of its potential deployments is in the form of smart home and ambient assisted living (SHAAL)to measure patients or elderly physiological signals, control home appliances, and monitor home. This paper focuses on the development of a wireless sensor node platform for SHAAL application over WSN which complies with the IEEE 802.15.4 standard and operates in 2.4 GHz ISM (industrial, scientific, and medical) band. The initial stage of SHAAL application development is the design of the wireless sensor node named TelG mote. The main features of TelG mote contributing to the green communications include low power consumption, wearable, flexible, user-friendly, and small sizes. It is then embedded with a self-built operating system named WiseOS to support customized operation. The node can achieve a packet reception rate (PRR) above 80% for a distance of up to 8 m. The designed TelG mote is also comparable with the existing wireless sensor nodes available in the market. 展开更多
关键词 smart home and ambient assisted living wireless sensor network wireless sensor node.
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