Pengenalan Ucapan Pelo Menggunakan Short Time Fourier Transform dengan Pemodelan Lightweight Convolutional Neural Network
https://doi.org/10.26594/teknologi.v15i1.5625
Abstract
Disartria adalah gangguan neurologis yang menghambat penderita untuk mengucapkan kata-kata dengan benar. Saat ini, Speech Command Recognition (SCR) turunan rumpun ilmu Automatic Speech Recognition (ASR) sedang diteliti dan dikembangkan untuk membantu penderita disartria atau pelo sehingga sulit berkomunikasi. Salah satu tahap dasar dalam membangun SCR adalah proses pengenalan kata, klasifikasi dan prediksi ucapan kata. Penelitian ini bertujuan untuk membangun pemodelan Deep learning, untuk pengenalan ucapan pelo, dengan desain arsitektur deep learning efisien yaitu Lightweight Convolutional Neural Network (LCNN), menggunakan ekstarksi ciri Short Time Fourier Transform STFT (STFT-LCNN). Augmentasi data dengan noise posisition dan re-pitch agar menambah variasi dataset yang efisien. Pendekatan arsitektur LCNN dihgunakan untuk implementasi edge devices kedepanya. Dalam pengimplementasiannya, model menggunakan 2 layer konvolusi, ekstraksi ciri menggunakan spektrogram, dan mennggunakan mini dataset dari EasyCall untuk pendeteksian, 5 kelas klasifikasi dengan penutur pelo atau disartria. Metode ini menghasilkan akurasi pemodelan sebesar 82%.
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Copyright (c) 2025 Andi Sugandi, Henry Ardian Irianta, Muhammad Fauzan Gustafic
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