Abstract

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.