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Mask estimation and imputation methods for missing data speech recognition in a multisource reverberant environment

机译：多源混响环境中用于丢失数据语音识别的模板估计和归类方法

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摘要

We present an automatic speech recognition system that uses a missing data approach to compensate for challenging environmental noise containing both additive and convolutive components. The unreliable and noise-corrupted ("missing") components are identified using a Gaussian mixture model (GMM) classifier based on a diverse range of acoustic features. To perform speech recognition using the partially observed data, the missing components are substituted with clean speech estimates computed using both sparse imputation and cluster-based GMM imputation. Compared to two reference mask estimation techniques based on inter-aural level and time difference-pairs, the proposed missing data approach significantly improved the keyword accuracy rates in all signal-to-noise ratio conditions when evaluated on the CHiME reverberant multisource environment corpus. Of the imputation methods, cluster-based imputation was found to outperform sparse imputation. The highest keyword accuracy was achieved when the system was trained on imputed data, which made it more robust to possible imputation errors.
机译：我们提出了一种自动语音识别系统，该系统使用丢失的数据方法来补偿具有挑战性的同时包含加性和卷积性成分的环境噪声。基于各种声学特征，使用高斯混合模型（GMM）分类器来识别不可靠且受噪声破坏的（“缺失”）组件。为了使用部分观察到的数据执行语音识别，将缺少的部分替换为使用稀疏插值和基于聚类的GMM插值计算的干净语音估计。与基于听觉水平和时间差对的两种参考掩模估计技术相比，在CHiME混响多源环境语料库上进行评估时，所提出的缺失数据方法在所有信噪比条件下均显着提高了关键字准确率。在插补方法中，发现基于簇的插补优于稀疏插补。当对插补数据进行系统训练时，关键字的准确性最高，这使其对可能的插补错误更加健壮。

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  • 来源
    《Computer speech and language》 |2013年第3期|798-819|共22页
  • 作者

    Sami Keronen; Heikki Kallasjoki; Ulpu Remes; Guy J. Brown; Jort F. Gemmeke; Kalle J. Palomaeki;

  • 作者单位

    Aalto University School of Science, Department of Information and Computer Science, PO Box 15400, Fl-00076 Aalto, Finland;

    Aalto University School of Science, Department of Information and Computer Science, PO Box 15400, Fl-00076 Aalto, Finland;

    Aalto University School of Science, Department of Information and Computer Science, PO Box 15400, Fl-00076 Aalto, Finland;

    University of Sheffield, Department of Computer Science, Regent Court, 211 Portobello St., Sheffield SI 4DP, UK;

    KU Leuven, Department ESAT-PSI, Kasteelpark Arenberg 10, 3001 Heverlee, Belgium;

    Aalto University School of Science, Department of Information and Computer Science, PO Box 15400, Fl-00076 Aalto, Finland;

  • 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
  • 关键词

    noise robust; speech recognition; missing data; binaural; multicondition; imputation;

    机译：噪音强语音识别;缺失数据;双耳多条件归责;

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