In this study, we present a deep learning-based speech signal-processing mobile application, called CITISEN, which can perform three functions: speech enhancement (SE), model adaptation (MA), and acoustic scene conversion (ASC). For SE, CITISEN can effectively reduce noise components from speech signals and accordingly enhance their clarity and intelligibility. When it encounters noisy utterances with unknown speakers or noise types, the MA function allows CITISEN to effectively improve the SE performance by adapting an SE model with a few audio files. Finally, for ASC, CITISEN can convert the current background sound into a different background sound. The experimental results confirmed the effectiveness of performing SE, MA, and ASC functions via objective evaluation and subjective listening tests. Moreover, the MA experimental results indicated that short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ) could be improved by approximately 5\% and 10\%, respectively. The promising results reveal that the developed CITISEN mobile application can be potentially used as a front-end processor for various speech-related services such as voice communication, assistive hearing devices, and virtual reality headsets. In addition, CITISEN can be used as a platform for using and evaluating the newly performed deep-learning-SE models, and can flexibly extend the models to address various noise environments and users.
View on arXiv