An alternative front-end for the AT&T WATSON LV-CSR system

In previously published work, we have proposed a novel feature extraction algorithm, based on the Teager-Kaiser energy estimates, that approximates human auditory characteristics and that is more robust to sub-band noise than the mean-square estimates of standard MFCCs. We refer to the novel features as Teager energy cepstrum coefficients (TECC). Herein, we study the TECC performance under additive noise and suggest how to predict the noisy TECC deviations by estimating the subband SNR values. Then, we report on the effectiveness of the TECCs when they are used hi the acoustic front-end of the state-of-the-art AT&T WATSON large-vocabulary recognizer. The TECC front-end is tested in the real-life voice-search Speak4it application for mobile devices. It provides a 6% relative word error rate reduction w.r.t. the MFCC front-end, using the same high performance language model, lexicon and acoustic model training.