## 3 Experimental Work and Results
### Results and Discussion  
Figure 4 shows the recognition accuracy, which is presented graphically for the proposed PNCC method in comparison to the state-of-the-art PNCC system, as well as the most commonly used baseline systems, such as GFCC, MFCC, and RASTA-PLP. The first graphs are obtained accuracy in the case of AWGN, while the rest of the graphs are for different types of environmental noise. The performance of these systems for each noise is shown in the case of clean data and at six SNR levels from \(-5\) dB to \(20\) dB with step size \(5\) dB.  
Figure 4: _Cont._  
As seen in Figure 4, it was concluded that the lowest robustness against noise acquired for MFCC and RASTA-PLP systems was interchangeable. With some types of noise, the performance of MFCC was higher than RASTA-PLP, and the reverse was true for other circumstances, while in the rest of the cases, the performance varied for each noise level. Replacing the mel triangular filter banks in MFCC processing by the gammatone filter banks in the GFCC system provided significantly measurable improvements in the recognition accuracy for all types of degradation. However, with significant types of noise, the GFCC performance was less than both of these systems at a SNR of \(-5\)dB. Using the time-frequency noise suppression technique, mean power normalization, and power function nonlinearity using (1/15) root remarkably improved the recognition accuracy in the PNCC systems in comparison to the last three systems. The recognition accuracy of the enhanced proposed PNCC dramatically outperformed all the systems for all noise types, specifically at low SNRs, whereas at high SNR values, the recognition accuracy was almost unchanged.  
Moreover, the proposed robustness technique against the noise did not degrade the system performance in the case of the undistorted speech waveform. Thus, the blurring effect from the large-time power averaging filter was almost unnoticeable. Figure 5 demonstrates an in-depth analysis of the percentage improvement rate of the proposed method in comparison to the other methods at low SNRs, such as \(-5\) dB, \(0\) dB, and \(5\) dB. Overall improvement in percentage recognition rate for all the types of noise was obtained in the case of MFCC, RASTA-PLP, and GFCC methods, and lastly PNCC method. In the SNR \(-5\) dB bar chart, the highest recognition rate in comparison to RASTA-PLP and GFCC methods was determined in the street noise condition.  
The proposed method outperformed the RASTA-PLP method by 37.21%, while it was better than the GFCC method by 38.66%. Differently, the highest recognition rate in comparison to MFCC and  
Figure 4: Percentage recognition accuracy under various noise conditions.  
PNCC methods was determined in the case of car noise. The proposed method was improved by 33.75% in the case of the MFCC method, while it was enhanced by 19.51% more than the PNCC method. In the SNR 0 dB bar chart, the highest percentage enhancement in recognition rate in comparison to MFCC and PNCC methods was obtained in the subway noise condition.  
The proposed method was better than the MFCC method by 49.75%, while it outperformed the PNCC method by 14.4%. Otherwise, the highest recognition rate compared to RASTA-PLP and GFCC methods was determined in the case of car noise. The proposed method was improved by 46.98% in  
Figure 5: Percentage improvement rate of the proposed method for all noise types at SNRs \(-\)5 dB, 0 dB, and 5 dB.  
the case of the RASTA-PLP method, while it was enhanced by 40.02% in the case of the GFCC method. In the SNR 5 dB bar chart, the highest recognition rate in comparison to MFCC, RASTA PLP, and PNCC methods was determined in the subway noise condition. The proposed method was better than the MFCC method by 55.72%, while it was better than the RASTA-PLP method by 47.87% and the PNCC method by 8.16%. Differently, the highest recognition rate in comparison to the GFCC method was determined in the case of exhibition noise. It was improved by 24.85% more than the GFCC method.