Alex Acero and Richard Stern
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.
|Published in||Proc. of International Conference on Acoustics, Speech, and Signal Processing|
|Publisher||Institute of Electrical and Electronics Engineers, Inc.|
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