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首页> 外文期刊>Proceedings of the IEEE >Conditional Random Fields in Speech, Audio, and Language Processing
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Conditional Random Fields in Speech, Audio, and Language Processing

机译:语音,音频和语言处理中的条件随机字段

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摘要

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.
机译:条件随机字段(CRF)是概率序列模型,在过去的十年中已应用于音频,语音和语言处理中的许多应用。在本文中,我们提供了CRF技术的教程概述,并指向其他资源以进行更深入的讨论;特别是,我们描述了常见的线性链模型以及CRF系列模型中的许多常见扩展。还概述了用于训练和评估这些模型的数学技术,并讨论了与其他概率模型的关系。最后,我们调查了语音,音频和语言处理方面的最新工作,以展示如何在不同的场景中部署相同的CRF技术。

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