# A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
## 4 Performance Evaluation  
### Experimental Environments and Method  
In the experiments, we aimed to measure (1) the accuracy of the presented five methods for the speech segment detection and (2) the execution time and accuracy of the proposed method and Wavenet-based denoising [13]. The results of the first experiment could be used to choose the best speech segment method for a target environment. To define each environment, we used various SNR levels and show the results in terms of various evaluation metrics, i.e., recall, precision, and F1 score.  
For the first experiment, we used clean speech data files generated in a quiet environment as the ideal answer. We then measured the recall, precision, and F1 score of the files where each speech segment detection method was applied for the noisy-speech files based on the clean data files.  
For the second experiment, we used the representative evaluation metrics for measuring the accuracy of denoising: SNR, STOI, and PESQ [43]. The proposed strategy reduced the overall length of the audio files due to the speech segment detection. Thus, we needed to consider how to compare the accuracy of denoising methods when their lengths were different. To resolve this problem, we  
Figure 20: The result of using the smoothing process in fuzzy clustering.  
designed three kinds of experiments. First, we attached the non-speech segments that were excluded by the speech segment detection in our proposed strategy into the final denoised results to align it with the result of Wavenet-based denoising results. It absolutely decreased the overall accuracy, but we showed that it was still effective even if non-speech segments without denoising were included in the final results. Second, we adjusted both clean and noisy-speech data files and the result of Wavenet-based denoising to have the same aligned length with the proposed strategy, i.e., the result of the speech segment detection. That is, we compared the speech segments only in both methods. Third, we compared the accuracy on the final results of methods while aligning the clean and noisy-speech data file with denoised results of each method. Even if the comparison segments between the methods became different, it was worth to show the final results of each method.  
We usde speech data sets provided by Edinburgh DataShare ([[https://datashare.is.ed.ac.uk/](https://datashare.is.ed.ac.uk/)]([https://datashare.is.ed.ac.uk/](https://datashare.is.ed.ac.uk/))). The training data files contained 4105 sentences spoken by ten native speakers, each in a noisy and quiet environment, respectively; the validation data files consisted of 824 sentences spoken by two native speaker in a quiet and noisy environment, respectively. Table 1 shows the characteristics of the data set used for the validation. We classified the entire data set into three groups according to the SNR. The dB range for the low SNR group was [\(-\infty\), 2]; that for the medium SNR group is (2, 10]; that for the high SNR group was (10, \(\infty\)]. The table shows the number of data sets, SNR, signal length, and portion of speech segments for each group. This implied that we needed to verify the effect of the speech segment detection and denoising with a variety of SNRs because the SNR fluctuated greatly for each group.  
For the experiment, we use an Amazon machine image equipped with 64 GB of RAM, 4 CPUs, Tesla K80 of GPU, and 10 GB of GPU memory.