Byung Hee Kang  Dong Kun Noh

10.5626/JOK.2023.50.4.344

speech enhancement environmental sound classification Deep Learning locationinformation

In the field of speech processing, deep learning has made great advances by improving the precision of speech recognition. One of advances, voice improvement, is a technique that can improve voice recognition by separating voice and noise from input mixed with speaking voice and noise. This is used in AI-speakers and smartphones to facilitate human-to-human communication and enable clean voice data collection for robots and text-to-speech. However, conventional speech enhancement techniques that use only a single model are not effective in eliminating noise that occurs specifically in each environment. To effectively eliminate environmental specific noise, this paper proposes a deep learning model that combines acoustic scene classification techniques with location information utilization techniques to enable optimal environmental-specific speech enhancements. As a result of the experiment, it is confirmed that this technique shows high voice quality improvement with low computational cost in various environments compared to the existing technique.