An efficient deep learning approach for automatic speech recognition using EEG signals.

 0 Người đánh giá. Xếp hạng trung bình 0

Tác giả: Babu Chinta, Moorthi M, Madhuri Pampana

Ngôn ngữ: eng

Ký hiệu phân loại: 006.454 Speech recognition

Thông tin xuất bản: England : Computer methods in biomechanics and biomedical engineering , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 167941

Electroencephalogram (EEG) signals enhance human-machine interaction but pose challenges in speech recognition due to noise and complexity. This study proposes an Efficient Deep Learning Approach (EDLA) integrating the Gannet Optimization Algorithm (GOA) and Elman Recurrent Neural Network (ERNN) for speaker identification. EEG data is preprocessed using a Savitzky-Golay filter, and key features are selected via recursive feature elimination. Evaluated on the Kara One dataset, EDLA achieves 95.2% accuracy, outperforming baseline methods. This framework advances EEG based speech recognition aiding brain-computer interfaces and assistive technologies for individuals with speech disorders.
Tạo bộ sưu tập với mã QR

THƯ VIỆN - TRƯỜNG ĐẠI HỌC CÔNG NGHỆ TP.HCM

ĐT: (028) 36225755 | Email: tt.thuvien@hutech.edu.vn

Copyright @2024 THƯ VIỆN HUTECH