Dilated Convolution for Time Series Learning
Wang Zhang, Subhro Das, et al.
ICASSP 2025
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.
Wang Zhang, Subhro Das, et al.
ICASSP 2025
Victor Akinwande, Megan Macgregor, et al.
IJCAI 2024
Rie Kubota Ando
CoNLL 2006
Fahiem Bacchus, Joseph Y. Halpern, et al.
IJCAI 1995