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.

PDF file

In  IEEE Trans. on Audio, Speech and Language Processing. Volume: 14 Issue: 6, Nov 2006. pp. 2109-2121

Publisher  Institute of Electrical and Electronics Engineers, Inc.
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