Teknologi Pengenalan Suara tentang Metode, Bahasa dan Tantangan: Systematic Literature Review
DOI:
https://doi.org/10.32877/bt.v7i2.1888
Keywords:
Deep learning, Machine learning, Pengenalan suara, PRISMA, SLR
Abstract
Dengan kemajuan teknologi kecerdasan buatan (AI) dan pembelajaran mesin, teknologi pengenalan suara akan terus berkembang. Dalam penelitian ini, akan dilakukan penelitian SLR (Systematic Literature Review) tentang pengenalan suara untuk mencari pembahasan tentang metode yang dipakai, bahasa yang diuji serta hambatan dan tantangan yang sering dihadapi. Sebanyak 2.400 artikel dikumpulkan dari 2 sumber data elektronik yang bersumber dari Scopus dan Semantic Scholar serta dalam rentang tahun 2020-2024, lalu disaring karena adanya duplikasi, tahapan kriteria inklusi dan eksklusi dan tahapan penilaian kualitas, sehingga didapat sekitar 32 artikel yang dipakai untuk menjawab tiga pertanyaan penelitian yang sudah dirumuskan melalui PICOC. Hasil yang didapat adalah terdapat 25 metode yang ditemukan dan metode CNN yang paling banyak dibahas dalam 6 artikel. Dari 28 bahasa yang ditemukan, bahasa Inggris merupakan bahasa yang paling banyak diuji dalam 7 artikel. Selain itu, terdapat 23 macam tantangan dan hambatan, yang paling banyak ditemui pada 17 artikel adalah sumber daya bahasa yang sedikit, dikarenakan hanya satu bahasa resmi dalam suatu negara dan ada pula yang hampir mengalami kepunahan sehingga tidak banyak tersedia untuk umum. Gangguan berupa kebisingan atau noise juga mengganggu dalam menyelesaikan penelitian tersebut. Lalu agar mencapai keakuratan yang tinggi dalam pengenalan suara, dibutuhkan data pelatihan yang besar. Penelitian SLR ini dapat mengidentifikasi tren dan metode terbaik dan terbukti efektif yang dapat dipakai di perangkat IoT, aplikasi smartphone, dan layanan cloud.
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