Towards Environment-Independent Spoken Language Systems

Abstract

In this paper we discuss recent results from our efforts to make SPHINX, the CMU continuous-speech speakerindependent recognition system, robust to changes in the environment. To deal with differences in noise level and spectral tilt between close-talking and desk-top microphones, we describe two novel methods based on additive corrections in the cepstral domain. In the first algorithm, an additive correction is imposed that depends on the instantaneous SNR of the signal. In the second technique, EM techniques are used to best match the cepstral vectors of the input utterances to the ensemble of codebook entries representing a standard acoustical ambience. Use of these algorithms dramatically improves recognition accuracy when the system is tested on a microphone other than the one on which it was trained.

Experimental results for baseline speech recognition performance using input acquired from a linear microphone array

“…It is widely accepted that appropriate data-acquisition technology must be available in order to make speech-recognition a viable computer input mode [1,2,3]. While work has been done in the area of signal conditioning [4], for the last three years, research at Brown University has been in progress to develop hardware, software and algorithms as a means to make non-intrusive speech acquisition a practical reality [5,6] Principal focus to date has been to use the phase relationships among a group of microphones spaced in a line -hence a linear array -for the remote, real-time acquisition of a talker's data. Various beamforming and talker location/tracking algorithms have been studied, reported, and evaluated relative to listening quality [7,8,9,10,11,12] The quality of a speech data acquisition system may be assessed in several ways.…”