# A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
## 5 Conclusions  
In this paper, we have proposed a preprocessing strategy for denoising of speech data based on the speech segment detection. A design of computationally efficient speech denoising is necessary to develop a scalable method for large-scale data sets. Furthermore, as the deep learning-based methods have been developed, its necessity becomes more important because they show the high performance in general while requiring significant costs. The basic idea of the proposed method is using the speech segment detection so as to exclude non-speech segments before denoising. The speech segmentation detection can exclude non-speech segments effectively, which will be removed in denoising process with a significant cost.  
As further study, we plan to incorporate the proposed strategy, i.e., effective preprocessing of denoising, to build a training model for denoising of the speech data. Two goals are (1) reducing the time to build the training model and (2) improving the denoising accuracy of the model. Here, the main issue will be that we need to figure out the characteristics of a data set for a target environment, e.g., the SNR type and evaluation metric, before building the training model so as to use the most effective speech segment detection method tailored to the target data set. Another issue is the investigation on constructing an adaptive model that learns the change of the characteristics of data sets because the most effective speech segment detection method becomes different as data sets are updated or new data are added.  
In this paper, we have investigated the speech segment detection methods for pre-processing of deep learning-based denoising, which require significant processing and training costs, and have improved the denoising speed by eliminating the segments that can be clearly determined by the speech segment detection. Significant overheads of the deep learning-based methods are valid in many other problems and domains as well. Especially, deep learning models in embedded devices such as mobile or IoT devices require efficient processing. The examples are the face recognition model on a single-board computer [44], real-time DNN model in mobile devices [45], and emotion recognition in Rasberry Pi [46]. As a result, the proposed strategy, i.e., pre-processing for excluding unnecessary parts with a negligible cost, which incur significant overhead in the deep learning process, can be adapted and investigated to the deep-learning based methods for the other problems.  
Conceptualization, S.-J.L. and H.-Y.K.; methodology, S.-J.L. and H.-Y.K.; validation, S.-J.L. and H.-Y.K.; data curation, S.-J.L.; writing--original draft preparation, S.-J.L. and H.-Y.K.; writing--review and editing, H.-Y.K.; supervision, H.-Y.K.; project administration, H.-Y.K.; funding acquisition, H.-Y.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018R1C1B5084424).  
The authors declare no conflict of interest.