Blind Source Extraction for Hands-Free Speech Recognition Based on Wiener Filtering and ICA-Based Noise Estimation
Yu Takahashi, Keiichi Osako, Hiroshi Saruwatari, Kiyohiro Shikano · 2008
In this paper, we proposed a new blind speech extraction method consisting of Wiener filtering and noise estimation based on independent component analysis (ICA). First, we provide both theoretically and experimental investigations on proficiency of ICA in noise estimation under a non-point-source noise condition. Next, computer simulation and experiment in an actual railway-station environment are conducted, and their results also indicate that ICA is proficient in noise estimation under a non-point-source noise condition. Finally, we newly propose a blind speech extraction method based on Wiener filtering and ICA-based noise estimation, and the effectiveness of the proposed method via speech recognition test in an actual railway-station environment.