• DOI:10.1109/TASL.2013.2244083
Corpus ID: 16585863

Machine Learning Paradigms for Speech Recognition: An Overview

@article{Deng2013MachineLP,
  title={Machine Learning Paradigms for Speech Recognition: An Overview},
  author={Li Deng and X. Li},
  journal={IEEE Transactions on Audio, Speech, and Language Processing},
  year={2013},
  volume={21},
  pages={1060-1089}
}

Li Deng, X. Li

Published 2013

Computer Science

IEEE Transactions on Audio, Speech, and Language Processing

Automatic Speech Recognition (ASR) has historically been a driving force behind many machine learning (ML) techniques, including the ubiquitously used hidden Markov model, discriminative learning, structured sequence learning, Bayesian learning, and adaptive learning. [...] Key Method These learning paradigms are motivated and discussed in the context of ASR technology and applications. We finally present and analyze recent developments of deep learning and learning with sparse representations, focusing on…Expand Abstract

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