Real-time Prototype for Integration of Blind Source Extraction and Robust Automatic Speech Recognition

This demo presents a real-time prototype for automatic blind source extraction and speech recognition in presence of multiple interfering noise sources. Binaural recorded mixtures are processed by a combined Blind/Semi-Blind Source Separation algorithm in order to obtain an estimation of the target signal. The recovered target signal is segmented and used as input to a real-time automatic speech recognition (ASR) system. Further, to improve the recognition performance, noise robust features based on Gammatone Frequency Cepstral Coefficients (GFCC) are used. The demo utilizes the data provided for the CHiME Pascal speech separation and recognition challenge and also real-time mixtures recorded on-site. Users will be able to listen to the recovered target signal and compare it with the original mixture and ASR output