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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Sriwijaya Electrical and Computer Engineering (Selco) Journal
Bhakti Yudho Suprapto
Teknik Elektro Universitas Sriwijaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Speaker Identification Using a Convolutional Neural Network Suci Dwijayanti; Alvio Yunita Putri; Bhakti Yudho Suprapto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (459.255 KB) | DOI: 10.29207/resti.v6i1.3795

Abstract

Speech, a mode of communication between humans and machines, has various applications, including biometric systems for identifying people have access to secure systems. Feature extraction is an important factor in speech recognition with high accuracy. Therefore, we implemented a spectrogram, which is a pictorial representation of speech in terms of raw features, to identify speakers. These features were inputted into a convolutional neural network (CNN), and a CNN-visual geometry group (CNN-VGG) architecture was used to recognize the speakers. We used 780 primary data from 78 speakers, and each speaker uttered a number in Bahasa Indonesia. The proposed architecture, CNN-VGG-f, has a learning rate of 0.001, batch size of 256, and epoch of 100. The results indicate that this architecture can generate a suitable model for speaker identification. A spectrogram was used to determine the best features for identifying the speakers. The proposed method exhibited an accuracy of 98.78%, which is significantly higher than the accuracies of the method involving Mel-frequency cepstral coefficients (MFCCs; 34.62%) and the combination of MFCCs and deltas (26.92%). Overall, CNN-VGG-f with the spectrogram can identify 77 speakers from the samples, validating the usefulness of the combination of spectrograms and CNN in speech recognition applications.
Real-time recognition of Indonesian sign language using recurrent neural network Yoel Andreas; Suci Dwijayanti; Hera Hikmarika; Bhakti Yudho Suprapto
Sriwijaya Electrical and Computer Engineering Journal Vol. 1 No. 1 (2024): Sriwijaya Electrical and Computer Engineering Journal
Publisher : Control and Computational Intelligent System (CoCIS) Research Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62420/selco.v1i1.1

Abstract

Hand gestures serve as a vital means of communication for deaf individuals. They often face communication challenges in their daily interactions due to the language barrier. This underscores the necessity of sign language interpreters. However, prevailing methods primarily rely on the Indonesian Sign Language System (SIBI), despite the widespread use of Indonesian Sign Language (BISINDO) for communication. Additionally, the effectiveness of these methods hinges greatly on the accuracy of feature extraction. To address this limitation, this study introduces a Recurrent Neural Network (RNN) approach for BISINDO interpretation. Data acquisition involved the use of a webcam to capture video data, subsequently transformed into frames andarrays. Collected from three respondents, the dataset comprises 3,240 videos and 97,200 array data points, encompassing letters and numbers. Among the tested parameters, training results indicate that utilizing the Adam optimizer with a learning rate of 0.0001 and 500 epochs yields the highest accuracy and minimal loss compared to other configurations. Subsequently, this modelunderwent real-time testing, conducted five times for 36 classes, achieving an accuracy of 81.67%. It is important to note that errors may arise due to similarities within hand signal language, particularly involving characters such as I, J, D, P, M, and N.
Co-Authors Alvio Yunita Putri Hera Hikmarika Suci Dwijayanti Suci Dwijayanti Yoel Andreas