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Mira Kartiwi
Department of Information Systems, Kulliyyah of ICT International Islamic University Malaysia

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Speech Emotion Recognition Using Deep Feedforward Neural Network Muhammad Fahreza Alghifari; Teddy Surya Gunawan; Mira Kartiwi
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 2: May 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v10.i2.pp554-561

Abstract

Speech emotion recognition (SER) is currently a research hotspot due to its challenging nature but bountiful future prospects. The objective of this research is to utilize Deep Neural Networks (DNNs) to recognize human speech emotion. First, the chosen speech feature Mel-frequency cepstral coefficient (MFCC) were extracted from raw audio data. Second, the speech features extracted were fed into the DNN to train the network. The trained network was then tested onto a set of labelled emotion speech audio and the recognition rate was evaluated. Based on the accuracy rate the MFCC, number of neurons and layers are adjusted for optimization. Moreover, a custom-made database is introduced and validated using the network optimized. The optimum configuration for SER is 13 MFCC, 12 neurons and 2 layers for 3 emotions and 25 MFCC, 21 neurons and 4 layers for 4 emotions, achieving a total recognition rate of 96.3% for 3 emotions and 97.1% for 4 emotions.Speech emotion recognition (SER) is currently a research hotspot due to its challenging nature but bountiful future prospects. The objective of this research is to utilize Deep Neural Networks (DNNs) to recognize human speech emotion. First, the chosen speech feature Mel-frequency cepstral coefficient (MFCC) were extracted from raw audio data. Second, the speech features extracted were fed into the DNN to train the network. The trained network was then tested onto a set of labelled emotion speech audio and the recognition rate was evaluated. Based on the accuracy rate the MFCC, number of neurons and layers are adjusted for optimization. Moreover, a custom-made database is introduced and validated using the network optimized.The optimum configuration for SER is 13 MFCC, 12 neurons and 2 layers for 3 emotions and 25 MFCC, 21 neurons and 4 layers for 4 emotions, achieving a total recognition rate of 96.3% for 3 emotions and 97.1% for 4 emotions.
Development of English Handwritten Recognition Using Deep Neural Network Teddy Surya Gunawan; Ahmad Fakhrur Razi Mohd Noor; Mira Kartiwi
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 2: May 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v10.i2.pp562-568

Abstract

Due to the advanced in GPU and CPU, in recent years, Deep Neural Network (DNN) becomes popular to be utilized both as feature extraction and classifier. This paper aims to develop offline handwritten recognition system using DNN. First, two popular English digits and letters database, i.e. MNIST and EMNIST, were selected to provide dataset for training and testing phase of DNN. Altogether, there are 10 digits [0-9] and 52 letters [a-z, A-Z]. The proposed DNN used stacked two autoencoder layers and one softmax layer. Recognition accuracy for English digits and letters is 97.7% and 88.8%, respectively. Performance comparison with other structure of neural networks revealed that the weighted average recognition rate for patternnet, feedforwardnet, and proposed DNN were 80.3%, 68.3%, and 90.4%, respectively. It shows that our proposed system is able to recognize handwritten English digits and letters with high accuracy.
Co-Authors Ahmad Fakhrur Razi Mohd Noor Muhammad Fahreza Alghifari Teddy Surya Gunawan Teddy Surya Gunawan