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首页> 外文期刊>Computer speech and language >Blind source extraction for robust speech recognition in multisource noisy environments
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Blind source extraction for robust speech recognition in multisource noisy environments

机译：盲源提取，用于在多源噪声环境中进行可靠的语音识别

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摘要

This paper proposes and describes a complete system for Blind Source Extraction (BSE). The goal is to extract a target signal source in order to recognize spoken commands uttered in reverberant and noisy environments, and acquired by a microphone array. The architecture of the BSE system is based on multiple stages: (a) TDOA estimation, (b) mixing system identification for the target source, (c) on-line semi-blind source separation and (d) source extraction. All the stages are effectively combined, allowing the estimation of the target signal with limited distortion. While a generalization of the BSE framework is described, here the proposed system is evaluated on the data provided for the CHiME Pascal 2011 competition, i.e. binaural recordings made in a real-world domestic environment. The CHiME mixtures are processed with the BSE and the recovered target signal is fed to a recognizer, which uses noise robust features based on Gammatone Frequency Cepstral Coefficients. Moreover, acoustic model adaptation is applied to further reduce the mismatch between training and testing data and improve the overall performance. A detailed comparison between different models and algorithmic settings is reported, showing that the approach is promising and the resulting system gives a significant reduction of the error rate.
机译：本文提出并描述了一个完整的盲源提取（BSE）系统。目的是提取目标信号源，以便识别在混响和嘈杂环境中发出并由麦克风阵列捕获的口头命令。 BSE系统的体系结构基于多个阶段：（a）TDOA估计，（b）目标源的混合系统标识，（c）在线半盲源分离和（d）源提取。所有阶段均有效地组合在一起，从而可以估计失真程度有限的目标信号。虽然描述了BSE框架的一般性，但是在此，建议的系统是根据为CHiME Pascal 2011竞赛提供的数据（即在现实世界的家庭环境中制作的双耳录音）进行评估的。 CHiME混合物用BSE进行处理，恢复的目标信号被馈送到识别器，该识别器使用基于伽马通频率倒谱系数的抗噪功能。此外，应用声学模型自适应可进一步减少训练数据与测试数据之间的不匹配，并改善整体性能。报告了不同模型和算法设置之间的详细比较，表明该方法很有希望，并且生成的系统可以显着降低错误率。

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  • 来源
    《Computer speech and language》 |2013年第3期|703-725|共23页
  • 作者

    Francesco Nesta; Marco Matassoni;

  • 作者单位

    Fondazione Bruno Kessler CIT-irst via Sommarive 18, 38123 Trento, Italy;

    Fondazione Bruno Kessler CIT-irst via Sommarive 18, 38123 Trento, Italy;

  • 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
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