Adaptive Weighting Of Microphone Arrays For Distant-Talking F0 And Voiced/Unvoiced Estimation

This paper introduces a new technique of multi-microphone processing which aims to provide features for the extraction of fundamental frequency and for the classification of voiced/unvoiced segments in distant-talking speech. A multi-channel periodicity function (MPF) is derived from an adaptive weighting of normalized and compressed magnitude spectra. This function highlights periodic clues of the given speech signals, even under noisy and reverberant conditions. The resulting MPF features are then exploited for voiced/unvoiced classification based on Hidden Markov Models. Experiments, conducted both on simulated data and on real seminar recordings based on a network of reversed T-shaped arrays, showed the robustness of the proposed technique.