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Discriminative training of HMMs for automatic speech recognition: A survey

机译：用于自动语音识别的HMM的歧视性培训：一项调查

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摘要

Recently, discriminative training (DT) methods have achieved tremendous progress in automatic speech recognition (ASR). In this survey article, all mainstream DT methods in speech recognition are reviewed from both theoretical and practical perspectives. From the theoretical aspect, many effective discriminative learning criteria in ASR are first introduced and then a unifying view is presented to elucidate the relationship among these popular DT criteria originally proposed from different viewpoints. Next, some key optimization methods used to optimize these criteria are summarized and their convergence properties are discussed. Moreover, as some recent advances, a novel discriminative learning framework is introduced as a general scheme to formulate discriminative training of HMMs for ASR, from which a variety of new DT methods can be developed. In addition, some important implementation issues regarding how to conduct DT for large vocabulary ASR are also discussed from a more practical aspect, such as efficient implementation of discriminative training on word graphs and effective optimization of complex DT objective functions in high-dimensionality space, and so on. Finally, this paper is summarized and concluded with some possible future research directions for this area. As a technical survey, all DT techniques and ideas are reviewed and discussed in this paper from high level without involving too much technical detail and experimental result.
机译：最近，判别训练（DT）方法在自动语音识别（ASR）中取得了巨大的进步。在这篇调查文章中，从理论和实践的角度对语音识别中所有主流DT方法进行了回顾。从理论上讲，首先介绍了许多有效的ASR判别学习准则，然后提出了一个统一的观点，以阐明最初从不同角度提出的这些流行的DT准则之间的关系。接下来，总结了用于优化这些准则的一些关键优化方法，并讨论了它们的收敛特性。此外，随着最近的一些进展，引入了新颖的判别性学习框架作为制定用于ASR的HMM判别性训练的一般方案，从中可以开发出多种新的DT方法。此外，还从更实际的方面讨论了有关如何对大词汇量ASR进行DT的一些重要实现问题，例如有效实施单词图的判别训练以及在高维空间中有效优化复杂的DT目标函数，以及以此类推。最后，对本文进行了总结和总结，并提出了该领域的一些未来研究方向。作为一项技术调查，本文不涉及太多技术细节和实验结果，而是从较高的角度对本文中的所有DT技术和思想进行了回顾和讨论。

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  • 来源
    《Computer speech and language》 |2010年第4期|p.589-608|共20页
  • 作者

    Hui Jiang;

  • 作者单位

    Department of Computer Science and Engineering, York University, 4700 Keele Street, Toronto, Ontario, Canada M3J 1P3;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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