Multi-microphone interference suppression using the principal subspace modification and its application to speech recognition

It has been shown that the principal subspace based multi-channel Wiener filter (MWF) provides better performance than the conventional MWF for the interference suppression in the case of a single target source. It can efficiently estimate the target component in the principal subspace and the acoustic transfer function up to a scaling factor. However, as input signal-to-interference ratio (SIR) becomes lower, larger errors are incurred in the estimation of the acoustic transfer function by the principal subspace degrading the performance in interference suppression. In order to alleviate this problem, a principal subspace modification method was proposed in previous work, which uses a priori information on the direction of the target signal. In this paper, a frequency-band dependent interpolation technique is further employed for the principal subspace modification and the speech recognition test is conducted to demonstrate the practical usefulness of the proposed methods as a front processing for the speech recognizer in interferer-present environment.