Modern multi-microphone hearing aids employ spatial filtering algorithms capable of enhancing speakers from one direction whilst suppressing interfering speakers of other directions. In this context, it is useful to track moving speakers in the acoustic space by linking disjoint speech segments. Since the identity of the speakers is not known beforehand, the system must match short speech segments without having a specific speaker model or prior knowledge of the speech content, while ignoring changes in acoustic conditions. In this paper, we present a method that matches each speech segment to non-specific speaker models thereby obtaining an activation pattern, and then compares the patterns of disjoint speech segments to each other. The proposed method is low in computational complexity and memory footprint and uses mel-frequency cepstral coefficients (MFCCs) and Gaussian mixture models (GMMs). We find that, when using MFCCs as acoustic features, the proposed speaker tracking method is robust to changes in the acoustic environment provided that sufficiently large segments of speech are available.
['Joachim Thiemann', 'Jörg Lücke', 'Steven van de Par']
Speaker tracking for hearing aids