Parameter sharing in subband likelihood-maximizing beamforming for speech recognition using microphone arrays

In this paper, we present methods to improve the computational efficiency of our previously proposed algorithm for microphone array processing for speech recognition, called subband likelihood-maximizing beamforming (S-LIMABEAM). In S-LIMABEAM, the parameters of a subband filter-and-sum beamformer are optimized to maximize the likelihood of the correct transcription of the utterance, as measured by the speech recognizer itself. This approach has been shown to produce significant improvements in recognition accuracy over conventional array processing methods in a variety of noisy and reverberant environments. However, because of the manner in which recognition features are computed, the number of subband parameters that have to be jointly optimized may be large, which slows the convergence of the algorithm. To address this problem, we present two methods of sharing parameters among multiple subband filters in order to significantly reduce the number of parameters to be optimized. Both of these methods exploit the spectral smoothing that occurs in the feature extraction process, but do so in different ways. By sharing parameters in the proposed manner, we are able to obtain a significant reduction in the time to convergence of S-LIMABEAM with a minimal degradation in speech recognition accuracy.