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All Journal International Journal of Electrical and Computer Engineering Majalah Forum Teknik UGM INOTEKS : Jurnal Inovasi Ilmu Pengetahuan, Teknologi, dan Seni Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Forum Nuklir Proceedings of KNASTIK Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Ilmiah Kursor Jurnal Teknologi Prosiding SNATIF Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Journal of Information Systems Engineering and Business Intelligence Proceeding of the Electrical Engineering Computer Science and Informatics Conference SENATIK STT Adisutjipto Yogyakarta Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) IJITEE (International Journal of Information Technology and Electrical Engineering) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Nasional Teknik Elektro dan Teknologi Informasi Pancaran Pendidikan Jurnal INFOTEL
Risanuri Hidayat
Departemen Teknik Elektro Dan Teknologi Informasi, Fakultas Teknik Universitas Gadjah Mada
Aerospace Engineering Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Physics

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Frequency domain analysis of MFCC feature extraction in children's speech recognition system Risanuri Hidayat
JURNAL INFOTEL Vol 14 No 1 (2022): February 2022
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v14i1.740

Abstract

Abstract ”The research on speech recognition systems currently focuses on the analysis of robust speech recognition systems. When the speech signals are combined with noise, the recognition system becomes distracted, struggling to identify the speech sounds. Therefore, the development of a robust speech recognition system continues to be carried out. The principle of a robust speech recognition system is to eliminate noise from the speech signals and restore the original information signals. In this paper, researchers conducted a frequency domain analysis on one stage of the Mel Frequency Cepstral Coefficients (MFCC) process, the Fast Fourier Transform (FFT), in children's speech recognition system. The FTT analysis in the feature extraction process determined the effect of frequency value characteristics utilized in the FFT output on the noise disruption. The analysis method was designed into three scenarios based on the value of the employed FFT points. The differences between scenarios were based on the number of shared FFT points. All FFT points were divided into four, three, and two parts in the first, second, and third scenarios, respectively. This study utilized children's speech data from the isolated TIDIGIT English digit corpus. As comparative data, the noise was added manually to simulate real-world conditions. The results showed that using a particular frequency portion following the scenario designed on MFCC affected the recognition system performance, which was relatively significant on the noisy speech data. The designed method in the scenario 3 (C1) version generated the highest accuracy, exceeded the accuracy of the conventional MFCC method. The average accuracy in the scenario 3 (C1) method increased by 1% more than all the tested noise types. Using various noise intensity values (SNR), the testing process indicates that scenario 3 (C1) generates a higher accuracy than conventional MFCC in all tested SNR values. It proves that the selection of specific frequency utilized in MFCC feature extraction significantly affects the recognition accuracy in a noisy speech.
A Lightweight Landmark-Based Model for Bahasa Lip-Reading Using Attention-BiLSTM Ferzha Putra Utama; Risanuri Hidayat; Syukron Abu Ishaq Alfarozi
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.269-282

Abstract

Background: Most lip-reading studies primarily utilize image-based representations of lip movements, offering extensive visual data while imposing significant computational burdens. Lip landmark-based representations are still not well understood, even though they could be a better way to describe lip dynamics in a smaller and more efficient way. This limitation is even more apparent in Bahasa lip-reading research, where there are few studies and computationally efficient solutions remain essential. Objective: This study examines the shortcomings of image-based lip-reading methods by leveraging lip landmarks as a concise and computationally efficient input representation. The proposed method is tested on the IndoLR open dataset, which contains video data of lip-reading in Bahasa. Methods: In this study, video sequences were transformed into coordinate-based landmark data to minimize computational demands while preserving critical information regarding lip dynamics. An attention-based BiLSTM model was trained to group10 word classes and 4 phrase classes using this dataset. Results: The model achieved accuracies of 93.03% for word classification and 95.18% for phrase classification. The approach also maintained high efficiency, with average inference times of 0.000530 and 0.011366 s per sample and computational costs of only 0.01 and 0.14 GFLOPs, respectively. Conclusion: These results show how well lip landmarks can be combined with a lightweight deep learning model with very few resources. This study makes a significant contribution to research on lip-reading in Bahasa and lays the groundwork for future studies that will use larger and more diverse datasets.   Keywords: Attention, BiLSTM, Bahasa, Landmark, Lip-Reading
Co-Authors Achmad Noerkhaerin Putra Achmad Rizal Adha Imam Cahyadi Adhi Susanto Agus Bejo, Agus Ahmad Fauzi Haqqoni Ahmad Nugroho Jati Akhmad Anggoro Akhmad Anggoro Akhyar Akhyar Akhyar Akhyar Albertus Joko Santoso Anggoro, Akhmad Anggun Fitrian Isnawati Anggun Winursito Bimo Sunarfri Hantono Brama Yoga Satria Budi Setiyanto Budi Setiyanto Budi Setiyanto Domy Kristomo Dwiyanto Dwiyanto Eka Firmansyah Eka Firmansyah Eka Wahyudi Eka Wahyudi Elfrida Ratnawati Ferzha Putra Utama Fitrah Sugita, Muhammad Gede Bayu Suparta Hanung Adi Nugroho Herdjunanto, Samiadji Hutagalung, Robertus Halomoan I Wayan Mustika Ida Bagus Verry Hendrawan Manuaba Ikhsan Shobari Indah Soesanti Irwan Novianto Isbadi Urifan Iswandi Jazi E Istiyanto Jazi E. Istiyanto Koko Joni Lasmadi, Lasmadi Lukito Edi Nugroho Mingsep Rante Sampebua Mohammad Yanuar Hariyawan Muhammad Fitrah Sugita Muhammad Oka Bagus Wibowo Muhammad Sonnie Bestian Nugroho, Anan Oyas Wahyunggoro Rachman, Anung Rachmawati Rachmawati Rachmawati Risky Via Yuliantari Robertus Halomoan Hutagalung Samiadji Herdjunanto Samiadji Herdjunanto Samiadji Herdjunanto Samuel Kristiyana Sani Tanaka Isnawanto Selo Sulistyo, Selo Siti Helmyati Sri Suning Kusumawardani Sujoko Sumaryono Sunarno Sunarno Sunarno Sunarno Sunarno Sunarno Sunarno Sunu Wibirama Syahroni Hidayat Syukron Abu Ishaq Alfarozi Teguh Bharata Adji Verry Hendrawan Manuaba, Ida Bagus Wening Mustikarini Willy Anugrah Cahyadi