In this paper, we introduce Subband LIkelihood-
MAximizing BEAMforming (S-LIMABEAM), a new microphone-
array processing algorithm specifically designed for speech
recognition applications. The proposed algorithm is an extension
of the previously developed LIMABEAM array processing algorithm.
Unlike most array processing algorithms which operate
according to some waveform-level objective function, the goal of
LIMABEAM is to find the set of array parameters that maximizes
the likelihood of the correct recognition hypothesis. Optimizing
the array parameters in this manner results in significant improvements
in recognition accuracy over conventional array processing
methods when speech is corrupted by additive noise and moderate
levels of reverberation. Despite the success of the LIMABEAM
algorithm in such environments, little improvement was achieved
in highly reverberant environments. In such situations where the
noise is highly correlated to the speech signal and the number of
filter parameters to estimate is large, subband processing has been
used to improve the performance of LMS-type adaptive filtering
algorithms. We use subband processing principles to design a
novel array processing architecture in which select groups of
subbands are processed jointly to maximize the likelihood of
the resulting speech recognition features, as measured by the
recognizer itself. By creating a subband filtering architecture that
explicitly accounts for the manner in which recognition features
are computed, we can effectively apply the LIMABEAM framework
to highly reverberant environments. By doing so, we are able
to achieve improvements in word error rate of over 20% compared
to conventional methods in highly reverberant environments.