A Beamforming Algorithm Based on Maximum Likelihood of a Complex Gaussian Distribution With Time-Varying Variances for Robust Speech Recognition

This letter addresses acoustic beamforming for robust speech recognition. Beamforming has been demonstrated to be one of the most effective approaches for robust recognition of distant speech using multi-microphones. In this letter, we derive a beamforming method, which we refer to as the “maximum-likelihood distortionless response (MLDR)” beamformer, based on the maximum-likelihood estimation (MLE) of a linear filter, with a distortionless constraint on the steering direction, assuming that the optimal beamformer outputs in the time-frequency domain follow a zero-mean complex Gaussian distribution with time-varying variances. By optimizing the beamformer output variances as well as the filter alternately with iterative update rules, and also by using the moving average of output powers at adjacent frames for robust estimation of an output variance, the MLDR beamformer may minimize the power of a relatively accurate noise component at the output, which resulted in better recognition performance than conventional beamformers. In addition, it can be further improved by initializing the output variances to averaged powers of a neural-network(NN)-masked input signal to estimate target speech powers, which achieved even better performance than compared beamformers exploiting trained NNs.