Learning Reduced Order Dynamics via Geometric Representations
Imran Nasim, Melanie Weber
SCML 2024
Speech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of statistical models for use in speech recognition. We give special attention to determining the parameters for such models from sparse data. We also describe two decoding methods, one appropriate for constrained artificial languages and one appropriate for more realistic decoding tasks. To illustrate the usefulness of the methods described, we review a number of decoding results that have been obtained with them. Copyright © 1983 by The Institute of Electrical and Electronics Engineers, Inc.
Imran Nasim, Melanie Weber
SCML 2024
Kellen Cheng, Anna Lisa Gentile, et al.
EMNLP 2024
Saurabh Paul, Christos Boutsidis, et al.
JMLR
P. Trespeuch, Y. Fournier, et al.
Civil-Comp Proceedings