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Conditional Random Fields in Speech, Audio, and Language Processing
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DOI：10.1109/JPROC.2013.2248112.png)
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Conditional random fields (CRFs) are probabilistic sequence models that have been applied in the last decade to a number of applications in audio, speech, and language processing. In this paper, we provide a tutorial overview of CRF technologies, pointing to other resources for more in-depth discussion; in particular, we describe the common linear-chain model as well as a number of common extensions within the CRF family of models. An overview of the mathematical techniques used in training and evaluating these models is also provided, as well as a discussion of the relationships with other probabilistic models. Finally, we survey recent work in speech, audio, and language processing to show how the same CRF technology can be deployed in different scenarios.
Keyword:
Automatic speech recognition (ASR)
natural language processing (NLP)
random fields
statistical learning
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25.9
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