Spectrum-entropy based beam-former with speaker tracking for hands-free continuous speech recognition in noise

In hands-free speech recognition of moving speakers, the time interval where the source position can be assumed stationary varies. It is very common for the speaker to move rapidly within the data window exploited. In such cases the conventional fixed-window direction of arrival (DOA) estimation may lead to poor tracking performance. In this paper we present a novel speech beamformer for moving speakers in noisy environments. The localization algorithm extracts a set of candidate DOA of the signal sources using array signal processing methods in the frequency domain. A minimum variance (MV) beamformer identifies the speech signal DOA in the direction where the signal´s spectrum entropy is minimized. The same localization algorithm is used to detect the closest direction to the initial estimation using a smaller window. The proposed method is evaluated using a phoneme recognition system and noise recordings from an air-condition fan and the TIMIT speech corpus. Extended experiments, carried out in the range of 25-0 dB SNR, show significant improvement in the recognition rate of moving speakers especially in very low SNR.