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All Journal JOIN (Jurnal Online Informatika) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Irawan, Eka Tresna
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Maleo-Short: An "In-the-Wild" Indonesian Dataset for Speaker Diarization Mardiana, Ardi; Muslimah, Dinda Desmonda; Bastian, Ade; Irawan, Eka Tresna
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1781

Abstract

Speaker diarization (SD), the task of partitioning an audio stream into speaker-homogenous segments, is fundamental for analyzing multi-speaker recordings. Its application to “in-the-wild” data, such as content from the YouTube platform, poses significant challenges, including overlapped speech, ambient noise, and rapid speaker turns, thereby constituting an active research area. While numerous SD datasets are available, they predominantly focus on English and other high-resource languages. A notable scarcity of publicly accessible datasets exists for the Indonesian language, as extant corpora are primarily engineered for Automatic Speech Recognition (ASR). To address this resource deficit, this research introduces Maleo-Short, a new Indonesian multi-speaker dataset derived from YouTube. The dataset comprises 110 short conversational clips, with a total duration of 1 hours 32 minutes. A reliable ground truth was established through a meticulous manual annotation process using ELAN to generate precise speaker segmentation and transcription files. To validate its utility and assess its complexity, the dataset was evaluated using pre-trained baseline models. The empirical results confirm its status as a challenging benchmark, with the most effective models achieving a Diarization Error Rate (DER) of 32.64% and a Word Error Rate (WER) of 33.78%. Maleo-Short is presented as a valuable, publicly accessible resource intended to catalyze advancements in Indonesian speaker diarization research by facilitating the development and rigorous evaluation of SD systems on acoustically complex and realistic conversational data. Maleo-Short is available at https://doi.org/10.57967/hf/7944.  
Maleo Emotion Audio Dataset Indonesia For Emotion Classification Mardiana, Ardi; Permana, Sri Mentari Widya Ningrum; Ii Sopiandi; Ade Bastian; Irawan, Eka Tresna
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2474

Abstract

The limited availability of voice emotion datasets in Indonesian poses a challenge in the development of Speech Emotion Recognition (SER) systems, even though the need for such systems continues to grow in various sectors such as customer service, education, and human-computer interaction. To address this challenge, this study developed the Maleo Emotion Audio Dataset, a collection of three-second audio clips labeled with seven emotion categories: angry, neutral, disgusted, sad, happy, afraid, and surprised. The data was collected from the YouTube platform, and the Maleo Emotion Dataset is available at https://huggingface.co/datasets/maleo-ai/maleo-emotion. It was processed through preprocessing, feature extraction, and augmentation stages. The five main features extracted include Zero Crossing Rate, energy, Mel-Frequency Cepstral Coefficients (MFCC), spectral roll-off, and spectral flux. To enhance generalization, augmentation techniques such as pitch shifting, noise injection, and time stretching were applied. The classification model was built using a Convolutional Neural Network (CNN) architecture with TensorFlow-based implementation. Evaluation showed that the model achieved 94.48% accuracy on the test data, with balanced performance across all emotion categories. These results demonstrate that the developed dataset and model architecture have high capability in effectively recognizing emotions from Indonesian speech in a locally relevant context.
Co-Authors Ade Bastian Ardi Mardiana Ii Sopiandi, Ii Muslimah, Dinda Desmonda Permana, Sri Mentari Widya Ningrum