Pengenalan Emosi Ucpan Berdasarkan CMARS dan SVM

Budi Triandi, Herman Mawengkang, Syahril Efendi

Abstract


Pengolahan data berdimensi tinggi merupakan permasalahan klasik dalam bidang penelitian pengenalan emosi ucapan, pengurangan ekstrak fitur dan model pengenalan emosi ucapan merupakan bagian terpenting. Makalah ini memilih fitur emosional yang valid dan mengekstrak nilai statistik fitur emosional. Model pengenalan emosi ucapan dibangun berdasarkan CMARS dan SVM efek pengurangan fitur masing-masing pada dua jenis model dibandingkan. Hasil eksperimen menunjukkan bahwa, berdasarkan fitur-fitur emosi yang diekstraksi dari dataset atau korpus emosi RAVDES memperoleh model terbaik dengan perolehan nilai 97% tingkat akurasi atas pengaruh variabel independen terhadap variabel dependen, pengurangan fitur dapat meningkatkan akurasi pengenalan dan efek pengenalan, model pengenalan emosi ucapan berbasis CMARS lebih baik dari pada SVM.


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DOI: http://dx.doi.org/10.30645/senaris.v4i2.227

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