Nhận diện người đọc Kinh Quran sử dụng NASNetLarge

Hebat-Allah Saber1, Ahmed Younes2,3, Mohamed Osman1,3, Islam Elkabani2,3,4
1Department of Mathematics and Computer Science, Faculty of Science, Damanhour University, Damanhour, Egypt
2Department of Mathematics and Computer Science, Faculty of Science, Alexandria University, Alexandria, Egypt
3Alexandria Quantum Computing Group, Faculty of Science, Alexandria University, Alexandria, Egypt
4Faculty of Computer Science and Engineering, Alamein International University, New Alamein, Egypt

Tóm tắt

Nhận diện người nói có những lợi thế đáng kể trong lĩnh vực tương tác giữa người và máy tính. Gần đây, nhiều học giả đã đóng góp trong lĩnh vực này và thành công trong việc tạo ra các mô hình học sâu cho hệ thống nhận diện người nói tự động. Tuy nhiên, hầu hết công việc xử lý tín hiệu giọng nói vẫn bị giới hạn ở các ứng dụng chỉ bằng tiếng Anh, mặc dù có nhiều thách thức với giọng nói tiếng Ả Rập, đặc biệt là với việc đọc Kinh Quran, sách thánh của Hồi giáo. Trong bối cảnh này, nghiên cứu này đề xuất một mô hình để nhận diện người đọc Kinh Quran bằng cách sử dụng một tập dữ liệu gồm 11.000 mẫu âm thanh được trích xuất từ 20 người đọc Quran. Để cho phép đưa các đại diện hình ảnh của mẫu âm thanh vào các mô hình được huấn luyện trước, các mẫu âm thanh được chuyển đổi từ đại diện âm thanh gốc sang đại diện hình ảnh bằng cách sử dụng Hệ số Cepstrum tần số Mel. Sáu mô hình học sâu được huấn luyện trước được đánh giá riêng biệt trong mô hình được đề xuất. Kết quả từ tập dữ liệu thử nghiệm cho thấy mô hình NASNetLarge đạt được tỷ lệ chính xác cao nhất là 98,50% trong số các mô hình đã được huấn luyện trước được sử dụng trong nghiên cứu này.

Từ khóa

#nhận diện người nói #Kinh Quran #mô hình học sâu #âm thanh #Hồi giáo

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