## 6 Speech Quality and Intelligibility Evaluation
### Results and Discussion  
In this section, we present the average scores of the objective measurements of the test set for the proposed system in comparison to the Wiener filter and single multi-layers DDAE. Pair of Wiener enhanced utterances and clean speech signals were used to train the composite-DDAE models instead of noisy-clean speech. To achieve the purpose of generalization, the noisy speech signal of the testing set was not the same used in the training set. For comparison purposes, we kept the output of the Wiener filter, and individual DDAE, and also trained another set of composite-DDAEs networks to directly map the noisy features to clean features. In the training phase, the noisy-clean speech signals were first converted into LPS and MFCC features. Based on the qualitative analysis results used in this work, our proposed hybrid approach achieved relative performance of 9% in terms of the PESQ. The results for the PESQ metric are presented in (Figure 11), which indicate that: (1) DDAE achieves better performance for SNR of 0 dB for white noise and SNR 0 and 5 dB for restaurant noise. Based on the experimental results we found that the individual DDAE network is unable to obliterate the noise due to local minima, and the enhanced signal residual noises. To overcome this issue, we need to increase the epoch in the training stage, which subsequently increases the training time. (2) The Wiener filter gives better results in pink noise of a 5 dB SNR. (3) The HC-DDAEs approach achieved better effects in most SNR levels of the rest of the types of noises. In our approach, the adjacent frames showed important effects on the clean frames reconstructions due to considering the dynamic structure. Figure 11: _Cont._The average PESQ scores of different types of noises for the (Wiener, individual DDAE, and HCDDAEs approaches in comparison with no approach) are presented in Tables 2 and 3. For pink noise, the Wiener filter provides better results for SNR 5, while the individual DDAE achieved almost the same results as the HC-DDAEs in SNR of 15 dB. The HC-DDAEs achieved the best results for the rest of the cases. In this section the results of different types of noises for (Wiener, DDAE, and HC-DDAEs approaches) are presented in terms of two objectives metrics, namely, HASQI and HASPI, using two sensorineural hearing loss audiograms (audiograms 4 and 5), more details of which can found in Table 1. (Figure 12) presents the HASQI score for the four different SNR levels (i.e., 0, 5, 10, and 15 dB) of experiment 1. Based on the experimental results, it is clear that the HC-DDAEs achieved significantly \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**(1)** & **White Noise** & **0** & **5** & **10** & **15** \\ \hline None & 1.34 & 1.54 & 1.73 & 2.12 \\ Wiener Filter & 1.64 & 2.02 & 2.2 & 2.34 \\ DDAE & 1.86 & 2.16 & 2.38 & 2.5 \\ HC-DDAEs & 1.28 & 1.38 & 1.49 & 2.09 \\ \hline
**(2)** & **Pink Noise** & **0** & **5** & **10** & **15** \\ \hline None & 1.34 & 1.61 & 1.8 & 2.12 \\ Wiener Filter & 1.69 & 1.9 & 2.25 & 2.4 \\ DDAE & 1.92 & 2.2 & 2.49 & 2.6 \\ HC-DDAEs & 1.87 & 1.98 & 2.53 & 2.62 \\ \hline
**(3)** & **Train Noise** & **0** & **5** & **10** & **15** \\ \hline None & 1.38 & 1.54 & 1.66 & 1.91 \\ Wiener Filter & 1.62 & 2.07 & 1.97 & 2.11 \\ DDAE & 2.02 & 2.16 & 2.26 & 2.5 \\ HC\_DDAEs & 2.87 & 2.41 & 2.41 & 2.69 \\ \hline
**(4)** & **Babble Noise** & **0** & **5** & **10** & **15** \\ \hline None & 1.4 & 1.59 & 1.8 & 1.91 \\ Wiener Filter & 1.67 & 2.09 & 1.99 & 2.09 \\ DDAE & 1.78 & 1.99 & 2.16 & 2.06 \\ HC\_DDAEs & 1.87 & 2.09 & 2.53 & 2.36 \\ \hline
**(5)** & **Restaurant Noise** & **0** & **5** & **10** & **15** \\ \hline None & 1.36 & 1.57 & 1.34 & 1.94 \\ Wiener Filter & 1.63 & 1.91 & 1.84 & 2.07 \\ DDAE & 1.92 & 2.28 & 1.89 & 1.89 \\ HC\_DDAEs & 1.91 & 2.19 & 2.29 & 2.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The PESQ results for Experiment 1 (average measures for the test set). Higher values represent better results. Figure 11: **The PESQ results (average measures for the test set).