Closely Coupled Array Processing and Model-Based Compensation for Microphone Array Speech Recognition

In conventional microphone array speech recognition, the array processor and the speech recognizer are loosely coupled. The only connection between the two modules is the enhanced target signal output from the array processor, which then gets treated as a single input to the recognizer. In this approach, useful environmental information, which can be provided by the array processor and also needs to be exploited by the recognizer, is ignored. Inherently, the array processor can generate multiple outputs of spatially filtered signals, as a multi-input-multi-output (MIMO) module. In this paper, a closely coupled approach is proposed, in which a recognizer with model-based noise compensation exploits the reference noise outputs from a MIMO array processor. Specifically, a multichannel model-based noise compensation is presented, including the compensation procedure using the vector Taylor series (VTS) expansion and parameter estimation using the expectation-maximization (EM) algorithm. It is also shown how to construct MIMO array processors from conventional beamformers. A number of practical implementations of the conventional loosely coupled approach and the proposed closely coupled approach were tested on a publicly available database, the Multichannel Overlapping Number Corpus (MONC). Experimental results showed that the proposed closely coupled approach significantly improved the speech recognition performance in the overlapping speech situations