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Bounded cepstral marginalization of missing data for robust speech recognition

机译：丢失数据的有界倒谱边缘化，可实现可靠的语音识别

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摘要

Spectral imputation and classifier modification can be counted as the two main missing data approaches for robust automatic speech recognition (ASR). Despite their potentials, little attention has been paid to the classifier modification techniques. In this paper, we show that transferring bounded marginalization, which is a classifier modification method, from spectral to cepstral domain would be beneficial for robust ASR. We also propose improved solutions on this transfer toward a better performance. Two such techniques are presented. The first approach still does not need training of any extra model. It benefits from an observed characteristic of cepstral features and raises accuracy of previously proposed method to a comparable level with that of a classic imputation method. The second technique combines our originally proposed method with an imputation technique but replaces spectral reconstruction with a simpler and faster possible range estimation of missing components. We show that the resulting method improves the accuracies of either of the two combined methods. The proposed techniques also show good robustness when implemented with an inaccurate spectrographic mask.
机译：频谱归因和分类器修改可以视为健壮的自动语音识别（ASR）的两种主要缺失数据方法。尽管具有潜力，但对分类器修改技术的关注却很少。在本文中，我们证明了将边界边缘化（一种分类器修改方法）从频谱域转移到倒频谱域将对鲁棒的ASR有所帮助。我们还针对此转移提出了改进的解决方案，以实现更好的性能。提出了两种这样的技术。第一种方法仍然不需要任何额外模型的训练。它得益于倒频谱特征的观察特性，将先前提出的方法的准确性提高到了与经典插补方法相当的水平。第二种技术将我们最初提出的方法与插补技术相结合，但是用更简单，更快速的可能丢失分量范围估计代替了频谱重建。我们表明，所得方法提高了两种组合方法中任一方法的精度。当用不正确的光谱掩模实施时，所提出的技术也显示出良好的鲁棒性。

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