5th International Conference on Spoken Language Processing
This paper addresses the problem of acoustic phonetic modeling. First, heterogeneous acoustic measurements are chosen in order to maximize the acoustic-phonetic information extracted from the speech signal in preprocessing. Second, classifier systems are presented for successfully utilizing high-dimensional acoustic measurement spaces. The techniques used for achieving these two goals can be broadly categorized as hierarchical, committee-based, or a hybrid of these two. This paper presents committee-based and hybrid approaches. In context-independent classification and context-dependent recognition on the TIMIT core test set using 39 classes, the system achieved error rates of 18.3% and 24.4%, respectively. These error rates are the lowest we have seen reported on these tasks. In addition, experiments with a telephone-based weather information word recognition task led to word error rate reductions of 10-16%.
Bibliographic reference. Halberstadt, Andrew K. / Glass, James R. (1998): "Heterogeneous measurements and multiple classifiers for speech recognition", In ICSLP-1998, paper 0396.