CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile Application

In this paper, we present a deep learning-based speech signal-processing mobile application, CITISEN, which can perform three functions: speech enhancement (SE), acoustic scene conversion (ASC), and model adaptation (MA). For SE, CITISEN can effectively reduce noise components from speech signals and accordingly enhance their clarity and intelligibility. For ASC, CITISEN can convert the current background sound to a different background sound. Finally, for MA, CITISEN can effectively adapt an SE model, with a few audio files, when it encounters unknown speakers or noise types; the adapted SE model is used to enhance the upcoming noisy utterances. Experimental results confirmed the effectiveness of CITISEN in performing these three functions via objective evaluation and subjective listening tests. The promising results reveal that the developed CITISEN mobile application can potentially be used as a front-end processor for various speech-related services such as voice communication, assistive hearing devices, and virtual reality headsets.

Boosting Objective Scores of Speech Enhancement Model through MetricGAN Post-Processing

The Transformer architecture has shown its superior ability than recurrent neural networks on many different natural language processing applications. Therefore, this study applies a modified Transformer on the speech enhancement task. Specifically, the positional encoding may not be necessary and hence is replaced by convolutional layers. To further improve PESQ scores of enhanced speech, the L_1 pre-trained Transformer is fine-tuned by MetricGAN framework. The proposed MetricGAN can be treated as a general post-processing module to further boost interested objective scores. The experiments are conducted using the data sets provided by the organizer of the Deep Noise Suppression (DNS) challenge. Experimental results demonstrate that the proposed system outperforms the challenge baseline in both subjective and objective evaluation with a large margin.