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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Future Artificial Intelligence and Technologies
Dhendra Marutho
Teknik Informatika, STMIK PROVISI Semarang
Computer Science & IT Engineering

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Enhanced multi-ethnic speech recognition using pitch shifting generative adversarial networks Nugroho, Kristiawan; Hadiono, Kristophorus; Sutanto, Felix; Marutho, Dhendra; Farooq, Omar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2904-2911

Abstract

Research in the field of speech recognition is a challenging research area. Various approaches have been applied to build robust models. A problem faced in speech recognition research is overfitting, especially if there is insufficient data to train the model. A large enough amount of data can train the model well, resulting in high accuracy. Data augmentation is an approach often used to increase the quantity of dataset. This research uses a data augmentation approach, namely pitch shifting, to increase the quantity of speech dataset, which is then processed into spectrogram data and then classified using a generative adversarial network (GAN). Using the pitch shifting-generative adversarial network (PS-GAN) model, this research produces high accuracy performance in multi-ethnic speech recognition, namely 98.43%, better than several similar studies.
Comprehensive Exploration of Machine and Deep Learning Classification Methods for Aspect-Based Sentiment Analysis with Latent Dirichlet Allocation Topic Modeling Setiadi, De Rosal Ignatius Moses; Marutho, Dhendra; Setiyanto, Noor Ageng
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-3

Abstract

This research explores the effectiveness of machine learning (ML) and deep learning (DL) classification methods in Aspect-Based Sentiment Analysis (ABSA) on product reviews, incorporating Latent Dirichlet Allocation (LDA) for topic modeling. Using the Amazon reviews dataset, this research tests models such as Naive Bayes (NB), Support Vector Machines (SVM), Random Forest (RF), Long Short-Term Memory (LSTM), and Gated Recurrent Units(GRU). Important aspects such as the product's quality, practicality, and reliability are discussed. The results show that the RF and DL models provide competitive performance, with the RF achieving an accuracy of up to 94.50% and an F1 score of 95.45% for the reliability aspect. The study's conclusions emphasize the importance of selecting an appropriate model based on specifications and data requirements for ABSA, as well as recognizing the need to strike a balance between accuracy and computational efficiency.
Co-Authors De Rosal Ignatius Moses Setiadi Dian Kristiawan Nugroho Farooq, Omar Kristophorus Hadiono Noor Ageng Setiyanto, Noor Ageng SUTANTO, FELIX