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

TL;DR

CITISEN is a mobile app utilizing deep learning for speech enhancement, model adaptation, and background noise conversion, demonstrating significant improvements in speech quality and intelligibility in noisy environments.

Contribution

The paper introduces CITISEN, a novel mobile application that integrates multiple deep learning functions for speech processing, including a unique background noise conversion feature for data augmentation.

Findings

Enhanced speech signals showed 6% and 33% improvements in STOI and PESQ.

Model adaptation further improved speech quality by approximately 6% and 11%.

Background noise conversion maintained scene classification accuracy, aiding data augmentation.

Abstract

This study presents a deep learning-based speech signal-processing mobile application known as CITISEN. The CITISEN provides three functions: speech enhancement (SE), model adaptation (MA), and background noise conversion (BNC), allowing CITISEN to be used as a platform for utilizing and evaluating SE models and flexibly extend the models to address various noise environments and users. For SE, a pretrained SE model downloaded from the cloud server is used to effectively reduce noise components from instant or saved recordings provided by users. For encountering unseen noise or speaker environments, the MA function is applied to promote CITISEN. A few audio samples recording on a noisy environment are uploaded and used to adapt the pretrained SE model on the server. Finally, for BNC, CITISEN first removes the background noises through an SE model and then mixes the processed speech with new background noise. The novel BNC function can evaluate SE performance under specific conditions, cover people's tracks, and provide entertainment. The experimental results confirmed the effectiveness of SE, MA, and BNC functions. Compared with the noisy speech signals, the enhanced speech signals achieved about 6\% and 33\% of improvements, respectively, in terms of short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ). With MA, the STOI and PESQ could be further improved by approximately 6\% and 11\%, respectively. Finally, the BNC experiment results indicated that the speech signals converted from noisy and silent backgrounds have a close scene identification accuracy and similar embeddings in an acoustic scene classification model. Therefore, the proposed BNC can effectively convert the background noise of a speech signal and be a data augmentation method when clean speech signals are unavailable.