Speaker Recognition Based on Feature Extraction in Clean and Noisy Environment
Ms. Manpreet Kaur · International Journal for Research in Applied Science and Engineering Technology · 2018
To improve the performance of speaker identification systems, an effective and robust method is proposed to extract features for speech processing, capable of operating in the clean and noisy environment. For capturing the characteristics of the signal, the Mel-frequency Cepstral Coefficient along with RASTA of the wavelet channels is calculated. Then the proposed feature extraction algorithm is evaluated on the speech database for text-dependent and text-independent speaker identification using the Gaussian Mixture Model (GMM) and Vector Quantization (VQ) identifier. Gaussian Mixture Models (GMMs) were used for the recognition stage as they give better recognition rate for the speaker's features than Vector Quantization. Some popular existing feature extraction methods MFCCs, LPC, LPC+DWT, MFCC+RASTA are also evaluated for comparison in this paper. Comparison of the proposed approach with the conventional feature extraction methods shows that the proposed method not only effectively reduces the influence of noise but also improves recognition accuracy. In addition, the performance of our method is very satisfactory in the noisy environment. A recognition rate of 98.63% was obtained using the proposed feature extraction technique.