# Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises
## 3 Results
### Comparison Algorithms  
In this session, the quality of speech enhancement using DNNs based on environmental noise classification was compared to the quality of the conventional hearing aid algorithm. In order to compare the quality of speech enhancement with the proposed algorithm, the results of the speech enhancement algorithm that were not based on the noise classification results and the conventional speech enhancement algorithm for hearing aids are presented in Table 2. The conventional speech enhancement algorithm for hearing aids classified the environmental noise using the Gaussian Mixture Models with covariance matrix, estimated the noise using MCRA by applying the optimized five parameters for each noise type, and reduced the noise using logMMSE [16].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Noise**} & **SNR** & \multicolumn{2}{c}{**PESQ Score**} & \multicolumn{2}{c}{**STOI Score**} & \multicolumn{2}{c}{**OQCM Score**} & \multicolumn{2}{c}{**LLR Score**} \\  & **(dB)** & **Before** & **After** & **Before** & **After** & **Before** & **After** & **Before** & **After** \\ \hline \multirow{4}{*}{Traffic} & 0 & 1.076 & 1.367 & 0.668 & 0.783 & 1.417 & 1.953 & 1.075 & 0.623 \\  & 5 & 1.171 & 1.666 & 0.775 & 0.854 & 1.721 & 2.371 & 0.834 & 0.454 \\  & 10 & 1.380 & 2.074 & 0.859 & 0.907 & 2.090 & 2.845 & 0.618 & 0.320 \\  & 15 & 1.754 & 2.559 & 0.920 & 0.944 & 2.566 & 3.348 & 0.428 & 0.225 \\ \hline \multirow{4}{*}{White} & 0 & 1.034 & 1.526 & 0.701 & 0.827 & 1.224 & 2.062 & 1.577 & 0.854 \\  & 5 & 1.058 & 1.880 & 0.811 & 0.894 & 1.378 & 2.479 & 1.491 & 0.729 \\  & 10 & 1.143 & 2.307 & 0.898 & 0.942 & 1.598 & 2.946 & 1.343 & 0.598 \\  & 15 & 1.351 & 2.769 & 0.953 & 0.971 & 1.933 & 3.430 & 1.133 & 0.472 \\ \hline \multirow{4}{*}{Average 1} & 0 & 1.071 & 1.421 & 0.668 & 0.794 & 1.396 & 2.024 & 1.057 & 0.585 \\  & 5 & 1.158 & 1.715 & 0.764 & 0.855 & 1.671 & 2.419 & 0.856 & 0.447 \\ \cline{1-1}  & 10 & 1.360 & 2.107 & 0.848 & 0.905 & 2.038 & 2.872 & 0.651 & 0.328 \\ \cline{1-1}  & 15 & 1.697 & 2.566 & 0.911 & 0.938 & 2.485 & 3.351 & 0.472 & 0.238 \\ \hline \hline \end{tabular}  
* Average 1: The average score of ten noises in each SNR dB.  
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Evaluation**} & **SNR** & **IMCRA + logMMSE** & **Proposed** & **Proposed** \\
**Measure** & **(dB)** & **with** & **DNNs** & **DNNs** \\  & **Classification** & **without Classification** & **with Classification** \\ \hline \multirow{4}{*}{PESQ score} & 0 & 1.320 & 1.324 & 1.421 \\  & 5 & 1.593 & 1.554 & 1.715 \\  & 10 & 1.978 & 1.921 & 2.107 \\  & 15 & 2.451 & 2.440 & 2.566 \\ \hline \multirow{4}{*}{STOI score} & 0 & 0.719 & 0.765 & 0.794 \\  & 5 & 0.807 & 0.839 & 0.855 \\  & 10 & 0.879 & 0.897 & 0.905 \\  & 15 & 0.931 & 0.939 & 0.942 \\ \hline \multirow{4}{*}{OQCM score} & 0 & 1.382 & 1.872 & 2.024 \\  & 5 & 1.734 & 2.230 & 2.419 \\  & 10 & 2.145 & 2.679 & 2.872 \\  & 15 & 2.720 & 3.223 & 3.351 \\ \hline \multirow{4}{*}{LLR score} & 0 & 0.726 & 0.684 & 0.585 \\  & 5 & 0.510 & 0.537 & 0.447 \\ \cline{1-1}  & 10 & 0.359 & 0.395 & 0.328 \\ \cline{1-1}  & 15 & 0.226 & 0.281 & 0.238 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation of speech quality by conventional and proposed speech enhancement based on noise classification.  
The PESQ and STOI scores of speech enhancement using the proposed DNNs without the classification result were similar to or slightly higher than the results of the speech enhancement using the conventional algorithm for hearing aids. However, the results of the speech enhancement using the proposed DNNs with the classification result produced higher PESQ, STOI, and OQCM and lower LLR scores.  
In particular, in the case of noisy speech with a low SNR, the improvement of speech quality was seen to be greater. Consequently, the speech enhancement for hearing aids can be expected to improve by applying different noise reduction algorithms depending on the type of noise.