Minimum mutual information beamforming for simultaneous active speakers

In this work, we address an acoustic beamforming application where two speakers are simultaneously active. We construct one subband domain beamformer in generalized sidelobe canceller (GSC) configuration for each source. In contrast to normal practice, we then jointly adjust the active weight vectors of both GSCs to obtain two output signals with minimum mutual information (MMI). In order to calculate the mutual information of the complex subband snapshots, we consider four probability density functions (pdfs), namely the Gaussian, Laplace, K
0
and Г pdfs. The latter three belong to the class of super-Gaussian density functions that are typically used in independent component analysis as opposed to conventional beamforming. We demonstrate the effectiveness of our proposed technique through a series of far-field automatic speech recognition experiments on data from the PASCAL Speech Separation Challenge. In the experiments, the delay-and-sum beamformer achieved a word error rate (WER) of 70.4 %. The MMI beamformer under a Gaussian assumption achieved 55.2 % WER which was further reduced to 52.0 % with a K
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pdf, whereas the WER for data recorded with close-talking microphone was 21.6 %.