Multivariate autoregressive modelling of multichannel reverberant speech

Recent research in speech localization and dereverberation introduced processing of the multichannel linear prediction (LP) residual of speech recorded with multiple microphones. This paper investigates the novel use of intra- and inter-channel speech prediction by proposing the use of a multichannel LP model derived from multivariate autoregression (MVAR), where current LP approaches are based on univariate autoregression (AR). Experiments were conducted on simulated anechoic and reverberant synthetic speech vowels and real speech sentences; results show that, especially at low reverberation times, the MVAR model exhibits greater prediction gains from the residual signal, compared to residuals obtained from univariate AR models for individually or jointly modelled speech channels. In addition, the MVAR model more accurately models the speech signal when compared to univariate LP of a similar prediction order and when a smaller number of microphones are deployed.