In this paper we report our initial efforts to make SPHINXth, e CMU

continuous-speech speaker-independent recognition system, robust to

changes in the environment. To deal with differences in noise level and

spectral tilt between closc-tcking atid desk-top microphones, we propose

two novel methods based on additive corrections in the cepstral domain.

In the first algorithm, the additive correction depends on the instantaneous

SNR of the signal. In the second technique, EM techniques are used to

bes~m atch the cepstral vectors of the input utter.mces to the ensemble of

codebook entries representing a standard acoustical ambience. Use of the

proposed algorithms dramatically improves recognition accuracy when

the system is tested on a microphone other than the one on which it was

trained.

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In  Proc. of International Conference on Acoustics, Speech, and Signal Processing

Publisher  Institute of Electrical and Electronics Engineers, Inc.
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