## 4 The Evaluation Results and Discussions
### Experimental Results and Discussions for the Proposed Method  
In this subsection, we provide the experimental results of our presented method and some discussions. For ease of discussion, the presented method is termed \"maximum state posterior probability\" with the abbreviation \"MSPP\". The experimental results for the multi-condition test utterances are further split into two parts: multi-condition training mode and clean-condition training mode. Note that the denoising framework in our method is with the help of the multi-condition training set, but we would like to see if the resulting enhanced speech features can behave well in both modes.  
Furthermore, we employ a comparative method for evaluation which primarily uses a deep neural network (DNN) to update the input FBANK features. This DNN is learned by directly minimizing the mean squared error (MSE) between the noisy and clean FBANK  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Signal-To-Noise Ratio (SNR)**} \\ \hline  & & \(\mathbf{-6}\) **dB** & \(\mathbf{-3}\) **dB** & **0 dB** & **3 dB** & **6 dB** & **12 dB** \\ \hline \multirow{4}{*}{PESQ} & baseline & 1.051 & 1.064 & 1.087 & 1.128 & 1.195 & 1.482 \\ \cline{2-7}  & MMSE & 1.103 & 1.103 & 1.198 & 1.350 & 1.520 & 1.967 \\ \cline{2-7}  & IRM & 1.051 & 1.064 & 1.087 & 1.128 & 1.195 & 1.493 \\ \hline \multirow{4}{*}{STOI} & baseline & 0.506 & 0.575 & 0.651 & 0.726 & 0.796 & 0.899 \\ \cline{2-7}  & MMSE & 0.515 & 0.579 & 0.638 * & 0.692 * & 0.743 * & 0.850 * \\ \cline{2-7}  & IRM & 0.506 & 0.575 & 0.652 & 0.726 & 0.796 & 0.901 \\ \hline \multirow{4}{*}{WER (\%)} & baseline & 65.3 & 61.7 & 55.1 & 48.2 & 41.5 & 31.1 \\ \cline{2-7}  & MMSE (TR,TS) & 68.2 * & 63.8 * & 57.8 * & 51.4 * & 44.3 * & 32.6 * \\ \cline{2-7}  & IRM (TR,TS) & 67.5 * & 64.7 * & 59.0 * & 52.9 * & 46.0 * & 35.3 * \\ \cline{2-7}  & MMSE (TS) & 68.3 * & 63.6 * & 57.3 * & 51.4 * & 44.9 * & 34.2 * \\ \cline{2-7}  & IRM (TS) & 65.9 * & 61.4 & 54.8 & 48.2 & 41.2 & 31.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The various evaluation results of MMSE, IRM, and the baseline for the test set in noise environment “**engine**”.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Signal-To-Noise Ratio (SNR)**} \\ \hline  & & \(\mathbf{-6}\) **dB** & \(\mathbf{-3}\) **dB** & **0 dB** & **3 dB** & **6 dB** & **12 dB** \\ \hline \multirow{4}{*}{PESQ} & baseline & 1.682 & 1.946 & 2.242 & 2.566 & 2.919 & 3.533 \\ \cline{2-7}  & MMSE & 2.087 & 2.413 & 2.739 & 3.059 & 3.323 & 3.812 \\ \cline{2-7}  & IRM & 1.773 & 2.044 & 2.350 & 2.680 & 3.024 & 3.624 \\ \hline \multirow{4}{*}{STOI} & baseline & 0.924 & 0.950 & 0.968 & 0.981 & 0.989 & 0.997 \\ \cline{2-7}  & MMSE & 0.917 * & 0.944 * & 0.962 * & 0.976 * & 0.983 * & 0.991 * \\ \cline{2-7}  & IRM & 0.932 & 0.955 & 0.970 & 0.982 & 0.990 & 0.997 \\ \hline \multirow{4}{*}{WER (\%)} & baseline & 31.4 & 27.8 & 25.9 & 23.8 & 23.0 & 21.9 \\ \cline{2-7}  & MMSE (TR,TS) & 32.3 * & 28.7 * & 26.4 * & 24.5 * & 23.9 * & 22.5 * \\ \cline{1-1} \cline{2-7}  & IRM (TR,TS) & 33.6 * & 30.4 * & 27.4 * & 25.2 * & 24.1 * & 22.9 * \\ \cline{1-1} \cline{2-7}  & MMSE (TS) & 32.9 * & 29.0 * & 27.2 * & 25.2 * & 24.4 * & 23.2 * \\ \cline{1-1} \cline{2-7}  & IRM (TS) & 32.4 * & 29.8 * & 26.6 * & 25.2 * & 23.8 * & 22.8 * \\ \hline \hline \end{tabular}
\end{table}
Table 3: The various evaluation results of MMSE, IRM, and the baseline for the test set in noise environment “**jackhammer**”.  
feature pairs in the multi-condition training set. This comparative method is termed feature-based MSE and abbreviated by \"FMSE\" in the following discussions.  
Before going into the experimental results, it is worth noting again that we used the original plain FBANK in the training set to prepare the acoustic models for both multi-condition and clean-condition training modes. Therefore, the various speech enhancement/robust feature extraction methods were just conducted on the test sets.