Online Accessed 1 September 2018.Authors who publish with this journal agree to the following terms: The Mel Frequency Scale and Coefficients. Gauhati University-Institute of Distance and Open Learning, Assam, India, 7:14068вЂ“14072, 2017.ДЇ. Hmm-dnn speech recognition techniques: a review. Extracting SpeakerвЂ™s Gender, Accent, Age and Emotional State from Speech. 500 Hours of Speech Recordings, with Speaker Demographics. Speaker Age Estimation Using I-Vectors, Engineering Applications of Artificial Intelligence. IEEE Transactions on Human-Machine Systems, 45, 2015. Selective Review and Analysis of Aging Effectsin Biometric System Implementation.

miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/. Mel Frequency Cepstral Coefficient (MFCC) Tutorial. An Analysis of The Influence of Deep Neural Network (DNN) Topology in Bottleneck Feature Based Language Recognition. Emotion Recognition from Chinese Speech for Smart Affective Services Using a Combination of Svm and Dbn. CUNY Graduate Center 365 Fifth Avenue, Room 4319 New York, USA, 2018.Д.Z.J.Z. Age Group Classification With Speech And Metadata Multimodality Fusion.

Journal of ELECTRICAL ENGINEERING, 2017.Д. GMM-Based Speaker Gender And Age Classification After Voice Conversion. Journal of ELECTRICAL ENGINEERING, 68:3вЂ“12, 2017. GMM-Based Speaker Age And Gender Classification In Czech And Slovak. speech-processing-for-machine-learning.html. Speech Processing for Machine Learning: Filter banks, Mel-Frequency Cepstral Coefficients (MFCCs) and WhatвЂ™s in-between. Aro, The Scientific Journal of Koya University, 3 (2), 24-29. Objective Gender and Age Recognition from Speech Sentences. College of Computer Science and Mathematics, University of Mosul, Mosul Iraq, 2016.ДЇ. Speaker Gender Recognition Using Hidden Markov Model. A New Pitch-Range Based Feature Set for a SpeakerвЂ™s Age and Gender Classification. THE SCHOOL OF ENGINEERING UNIVERSITY OF BRIDGEPORT CONNECTICUT, 2017.Д«. SA Framework for Enhancing Speaker Age and Gender Classification by Using a New Feature Set and Deep Neural Network Architectures. Kata Kunci:В Klasifikasi, Mel-Frequency Cepstrum Coefficient, Acoustic Models, Gaussian Mixture Model, Hidden Markov ModelД.

Hasil eksperimen menunjukkan bahwa model GMM-HMM yang telah dibangun mampu melakukan klasifikasi usia-genderdengan akurasi hingga 96,4%.В Model ini dapat diperbaiki denganВ pengaturan parameterВ secara lebih presisi dan penggunaan datasetВ yang lebih besar. Pada penelitian ini, basisdata suara diambil dari situs Common Voice, yang berisi banyak posting blog, buku-buku lama, film, dan pidato publik lainnya. Terakhir, HMM diterapkan untuk mendeteksiВ genderdan kelompok usia. Selanjutnya, dilakukan pelatihan untuk menghasilkan model akustik untuk semua penutur (pria dan wanita dari berbagai usia) di dalam basisdata pelatihan. Pertama, dilakukan pembangunan vektor ciri menggunakan Mel-Frequency Cepstrum CoefficientВ (MFCC). Penelitian ini berfokus pada klasifikasi usia-gender berdasarkan suara pembicara menggunakan gabungan Gaussian Mixture Modeldan Hidden Markov Model(GMM-HMM). Pengelompokan usia yang berbeda dibagi menjadi tiga kelompok: anak, muda, menengah, dan senior berdasarkan rentang usia tertentu. KlasifikasiВ genderjuga telah diterapkan dalam pengenalan wajah, peringkasan video, penentuan tingkat izin yang berbeda untuk kelompok umur yang berbeda, dan lainnya. Klasifikasi usia-genderberdasarkan suara sangat berguna dalam perkenalan pidato dan dalam pengenalan emosi.

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