Semi-blind suppression of internal noise for hands-free robot spoken dialog system

Abstract-The speech enhancement architecture presented in this paper is specifically developed for hands-free robot spoken dialog systems. It is designed to take advantage of additional sensors installed inside the robot to record the internal noises. First a modified frequency domain blind signal separation (FD-BSS) gives estimates of the noises generated outside and inside of the robot. Then these noises are canceled from the acquired speech by a multichannel Wiener post-filter. Some experimental results show the recognition improvement for a dictation task in presence of both diffuse background noise and internal noises

An Improved permutation solver for blind signal separation based front-ends in robot audition

IROS2008: IEEE/RSJ International Conference on Intelligent Robotics and Systems , September 22-26, 2008, Nice, France.The model of the human/machine hands-free speech interface is defined as a point source (the user voice) and a diffuse background noise. This situation is very different from the usual cocktail party model, separation of a mixture of speeches, that is usually treated in frequency domain blind signal separation (FD-BSS). In particular, the fast permutation solvers proposed for the cocktail party model results in poor separation performance in this case. In order to resolve the permutation more efficiently, this paper proposes a new approach that exploits the statistical discrepancy between the target speech and the diffuse background noise