## 4 The Evaluation Results and Discussions
### The Effect of SE Methods in Speech Recognition for Noisy Speech  
In this subsection, we would like to investigate whether the signals pre-enhanced by speech enhancement (SE) methods can result in speech features that bring about improved recognition accuracies relative to the baseline. We selected two SE methods: MMSE [39] (an unsupervised approach) and ideal ratio mask (IRM) [10] (a supervised approach) and conducted the evaluation experiments with a multi-condition mode in two different arrangements:  
1. The SE process was conducted on the utterances in both the training and test sets. 2. The SE process was only conducted on the utterances in the test set, while the training set remained unchanged. Tables 1-3 list the word error rates (WER in %) for the above two arrangements with respect to the test utterances of three noise types (white, engine, and jackhammer). In these tables, MMSE (TR, TS) and IRM (TR, TS) refer to the first arrangement, in which both the training and test sets were enhanced by MMSE/IRM, while MMSE (TS) and IRM (TS) refer to the second arrangement, in which MMSE/IRM enhanced only the test set. Furthermore, to see if the two SE methods can improve the quality and intelligibility of the test utterances as they promise, we also list the PESQ and STOI scores in these tables. From the results shown in these three tables, we have the following observations:  
1. Both MMSE and IRM improve the PESQ scores, while MMSE behaves better than IRM in most cases. For example, MMSE obtains 1.647 and 1.967 in PESQ at the 12 dB SNR case of Tables 1 and 2, while the corresponding values achieved by IRM are 1.275 and 1.493. This is possibly due to the mismatch between the training and test sets for IRM. However, neither MMSE nor IRM can bring a significant STOI upturn for almost all noise scenarios. In addition, IRM results in less STOI degradation than MMSE. 2. Comparing the two first arrangements (\"TR, TS\" and \"TS\") as for MMSE and IRM, we find that conducting the SE methods on both the training and test sets shows inferior recognition performance (higher WERs) than conducting them only on the test set. Furthermore, the first arrangement \"TR, TS\" even causes worse results than the baseline. For example, the methods \"MMSE (TR, TS)\" and \"MMSE (TS)\" obtain 56.5% and 55.0% WERs at the 3 dB SNR case of Table 1, which are worse than the baseline 49.8%. The underlying explanation is that MMSE and IRM, when conductedon the training set, result in an over-smooth trajectory for speech utterances in the time or spectral domains and therefore reduce the diversity and discriminant capability of the various phone realizations in the training set. 3. As for the second arrangement (i.e., MMSE and IRM conducted only on the test set), IRM appears to bring moderately lower WERs for the two noise types \"white\" and \"engine\" (shown in the lower part of Tables 1 and 2), while it is not quite helpful for the \"jackhammer\" case (shown in the lower part of Table 3). Comparatively, MMSE worsens the recognition accuracy for all three noise situations. Based on the above observations, we may conclude with the following points: 1. The two metrics, PESQ and WER, seem to have little correlation since the method MMSE, which causes better PESQ scores, results in higher WERs (except for some low SNR cases). That is, improving the speech quality does not necessarily reduce its recognition accuracy. Relative to PESQ, the STOI index has more to do with the recognition accuracy in more noisy situations. This can be observed in that the method IRM can improve STOI and recognition accuracy simultaneously for \"white\" and \"engine\" noises, even though the improvement is somewhat marginal. In addition, the increase in STOI does not necessarily reduce WER, while the reduction in WER always comes with the increase in STOI. 2. The two SE methods used here do not show obvious benefits of speech recognition in noisy situations.