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Comparing human and automatic speech recognition in a perceptual restoration experiment

机译：在感知恢复实验中比较人为和自动语音识别

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摘要

Speech that has been distorted by introducing spectral or temporal gaps is still perceived as continuous and complete by human listeners, so long as the gaps are filled with additive noise of sufficient intensity. When such perceptual restoration occurs, the speech is also more intelligible compared to the case in which noise has not been added in the gaps. This observation has motivated so-called 'missing data' systems for automatic speech recognition (ASR), but there have been few attempts to determine whether such systems are a good model of perceptual restoration in human listeners. Accordingly, the current paper evaluates missing data ASR in a perceptual restoration task. We evaluated two systems that use a new approach to bounded marginalisation in the cepstral domain, and a bounded conditional mean imputation method. Both methods model available speech information as a clean-speech posterior distribution that is subsequently passed to an ASR system. The proposed missing data ASR systems were evaluated using distorted speech, in which spectro-temporal gaps were optionally filled with additive noise. Speech recognition performance of the proposed systems was compared against a baseline ASR system, and with human speech recognition performance on the same task. We conclude that missing data methods improve speech recognition performance in a manner that is consistent with perceptual restoration in human listeners.
机译：通过引入频谱或时间间隙而被扭曲的语音仍然可以被听众感知为连续且完整的声音，只要间隙中充满了足够强度的附加噪声即可。当这种感觉恢复发生时，与在间隙中未添加噪声的情况相比，语音也更加清晰。这种观察激发了用于自动语音识别（ASR）的所谓“丢失数据”系统，但是几乎没有尝试确定这种系统是否是人类听众感知恢复的良好模型。因此，当前论文评估了感知还原任务中的缺失数据ASR。我们评估了两个系统，它们使用一种新方法来倒谱域中的有界边际化和有条件的条件均值插补方法。两种方法都将可用语音信息建模为清晰语音的后验分布，然后将其传递给ASR系统。拟议的缺失数据ASR系统是使用失真的语音进行评估的，其中频谱时空间隙可选地由加性噪声填充。将拟议系统的语音识别性能与基准ASR系统进行了比较，并与同一任务上的人类语音识别性能进行了比较。我们得出的结论是，丢失的数据方法以与人类听众的知觉恢复相一致的方式提高了语音识别性能。

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  • 来源
    《Computer speech and language》 |2016年第1期|14-31|共18页
  • 作者

    Ulpu Remes; Ana Ramirez Lopez; Lauri Juvela; Kalle Palomaeki; Guy J. Brown; Paavo Alku; Mikko Kurimo;

  • 作者单位

    Department of Signal Processing and Acoustics, Aalto University School of Electrical Engineering, PO Box 13000, Espoo, Finland;

    Department of Signal Processing and Acoustics, Aalto University School of Electrical Engineering, PO Box 13000, Espoo, Finland;

    Department of Signal Processing and Acoustics, Aalto University School of Electrical Engineering, PO Box 13000, Espoo, Finland;

    Department of Signal Processing and Acoustics, Aalto University School of Electrical Engineering, PO Box 13000, Espoo, Finland;

    Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello Street, Sheffield S1 4DP, United Kingdom;

    Department of Signal Processing and Acoustics, Aalto University School of Electrical Engineering, PO Box 13000, Espoo, Finland;

    Department of Signal Processing and Acoustics, Aalto University School of Electrical Engineering, PO Box 13000, Espoo, 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

    Automatic speech recognition; Missing data; Observation uncertainties; Perceptual restoration; Uncertainty propagation;

    机译：自动语音识别;缺失数据;观测不确定性;知觉恢复;不确定性传播;

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