Safi, M., Abbas, E. (2023). Speech Recognition Algorithm in a Noisy Environment Based on Power Normalized Cepstral Coefficient and Modified Weighted-KNN. , 41(8), 1107-1117. doi: 10.30684/etj.2023.140643.1469
Mohammed E. Safi; Eyad I. Abbas. "Speech Recognition Algorithm in a Noisy Environment Based on Power Normalized Cepstral Coefficient and Modified Weighted-KNN". , 41, 8, 2023, 1107-1117. doi: 10.30684/etj.2023.140643.1469
Safi, M., Abbas, E. (2023). 'Speech Recognition Algorithm in a Noisy Environment Based on Power Normalized Cepstral Coefficient and Modified Weighted-KNN', , 41(8), pp. 1107-1117. doi: 10.30684/etj.2023.140643.1469
Safi, M., Abbas, E. Speech Recognition Algorithm in a Noisy Environment Based on Power Normalized Cepstral Coefficient and Modified Weighted-KNN. , 2023; 41(8): 1107-1117. doi: 10.30684/etj.2023.140643.1469

Speech Recognition Algorithm in a Noisy Environment Based on Power Normalized Cepstral Coefficient and Modified Weighted-KNN

Engineering and Technology Journal
Volume 41, Issue 8, 2023, Pages 1107-1117    PDF (919.24 K)
Document Type: Research Paper
DOI: 10.30684/etj.2023.140643.1469
Authors
Mohammed E. Safi* ; Eyad I. Abbas
Electrical Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.
Abstract
Speech recognition is widely used in robot control and automation. Nevertheless, the use of speech recognition in robots is limited due to its susceptibility to background noise. This paper proposes a speech recognition algorithm to control robots in noisy environments. The proposed algorithm is based on Perceptual Linear Predictive Cepstral Coefficients (PNCC), which is a noise-resistant feature extraction technique, and Modified K-Nearest Neighbors (KNN) with Dynamic Time Warping (DTW) as the classifier. A new KNN-DTW classifier is proposed, integrating weighted KNN and DTW. The proposed algorithm results from experiments comparing PNCC and Mel-frequency cepstral coefficients (MFCC) feature extraction techniques with different classifiers, namely KNN-DTW, two types of KNN (weighted KNN and Medium-KNN), and two types of Support Vector Machine SVM (Linear SVM and Quadratic SVM). The database used to investigate the accuracy was the audio-visual data corpus database UOTletters, which includes 30 speakers, 26 English letters, and 1560 utterances. The database is divided into 50% for training and 50% for testing purposes. In a noise-free environment, the accuracy of the proposed algorithm reached 100%. Moreover, the proposed algorithm demonstrates greater noise immunity across all five noise levels, with an average accuracy difference of 13.67% compared to baseline algorithms.

Highlights
  • The proposed algorithm is based on PNCC feature extraction with a new classifier Weighted-KNN-DTW.
  • Weighted-KNN-DTW classifier is a modification of Weighted KNN and DTW.
  • The accuracy of the proposed algorithm was calculated with different levels of white noise (20dB, 15dB, 10dB, and 5dB).
Keywords
Speech Recognition; PNCC; MFCC; KNN; DTW; SVM
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