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
    245 Citations
    Speech recognition in a dialog system: from conventional to deep processing
    • 8
    • Highly Influenced
    Unsupervised adaptation of ASR systems: An application of dynamic programming in machine learning
    • 1
    • Highly Influenced
    A Review on Automatic Speech Recognition Architecture and Approaches
    • 42
    • PDF
    Advanced Data Exploitation in Speech Analysis: An overview
    • 39
    • PDF
    Analysing acoustic model changes for active learning in automatic speech recognition
    • 1
    • PDF
    Advances in Artificial intelligence Using Speech Recognition
    • 9
    Transfer learning for speech and language processing
    • D. Wang, T. Zheng
    • Computer Science
    • 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)
    • 2015
    • 116
    • PDF

    References

    SHOWING 1-10 OF 300 REFERENCES
    Active learning: theory and applications to automatic speech recognition
    • 138
    • PDF
    Structured Discriminative Models For Speech Recognition: An Overview
    • 27
    Template-Based Continuous Speech Recognition
    • 173
    • PDF
    Automatic Speech Recognition Based on Non-Uniform Error Criteria
    • 13
    • PDF
    Connectionist Speech Recognition: A Hybrid Approach
    • 1,356
    Robust continuous speech recognition using parallel model combination
    • 533
    • PDF
    Discriminative Learning for Speech Recognition: Theory and Practice
    • X. He, Li Deng
    • Computer Science
    • Discriminative Learning for Speech Recognition
    • 2008
    • 21
    • PDF
    Structured SVMs for Automatic Speech Recognition
    • S. Zhang, M. Gales
    • Computer Science
    • IEEE Transactions on Audio, Speech, and Language Processing
    • 2013
    • 39
    • PDF
    Unsupervised and active learning in automatic speech recognition for call classification
    • 45
    • PDF
    Unsupervised training of a speech recognizer: recent experiments
    • 133
    • PDF