Fuzzy Neural Network with Audio-Visual Data for Voice Activity Detection in Noisy Environments

Abstract

Voice activity detection is a fundamental problem in speech processing, which has been discussed for decades. However, it is a big challenge to determine the speech boundary in noisy environments because the corrupted speech is uncertain. In handing problems with noisy data, this study adopts a fuzzy neural network (FNN) to process the uncertainty. Furthermore, human speech perception is bimodal. We lip-read in noisy environments to improve intelligibility. This idea inspires us to adopt the visual information into the voice activity detection system. Based on the skin color segmentation, faces and mouths can be found in images. By analyzing the geometric shapes, the lip contour feature of speaker can be extracted. Then, the proposed fuzzy neural network considers not only audio but also visual information. Compared with the other voice activity detection, the proposed method for voice activity detection is more robust in the condition of low signal-to-noise ratio (SNR).