Identifikasi Ucapan Disartria Menggunakan Arsitektur Convolutional Neural Network MobileNetV2 dan MFCC
https://doi.org/10.26594/teknologi.v16i1.5842
Abstract
Seiring meningkatnya kebutuhan Automatic Speech Recognition (ASR) pada edge device, pengembangan sistem deteksi gangguan bicara menjadi semakin relevan, terutama untuk aplikasi pada alat kesehatan. Deteksi dini disartria memegang peranan krusial dalam intervensi klinis, sehingga diperlukan model klasifikasi yang efisien agar kedepanya dapat diimplementasikan pada edge atau embedded system untuk membantu proses diagnosis. Penelitian ini bertujuan untuk mengimplementasikan dan mengevaluasi sebuah model deep learning yaitu lightweight Convolutional Neural Network (LCNN) untuk klasifikasi ucapan disartria dengan memanfaatkan arsitektur MobileNetV2 melalui pendekatan transfer learning. Metode penelitian menggunakan dataset publik UASpeech, di mana fitur akustik diekstraksi menggunakan Mel-Frequency Cepstral Coefficients (MFCC) untuk menghasilkan 40 koefisien. Fitur MFCC kemudian divisualisasikan sebagai spektrogram untuk melatih model MobileNetV2 yang sebelumnya telah dilatih pada dataset berskala besar. Hasil evaluasi terbaik pada data uji menunjukkan performa yang sangat baik dengan pencapaian akurasi sebesar 95%
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Copyright (c) 2026 Henry Ardian Irianta, Abdul Fadlil , Rusydi Umar
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