Speech Recognition Algorithm in a Noisy Environment Based on Power Normalized Cepstral Coefficient and Modified Weighted-KNN
| Autor: | Mohammed Safi, Eyad Abbas |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Engineering and Technology Journal, Vol 41, Iss 8, Pp 1107-1117 (2023) |
| Druh dokumentu: | article |
| ISSN: | 1681-6900 2412-0758 |
| DOI: | 10.30684/etj.2023.140643.1469 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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