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On-line Stochastic Matching compensation for non-stationary noise

机译：非平稳噪声的在线随机匹配补偿

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摘要

This paper treats the problem of noise compensation in speech recognition when training and testing conditions do not match. We are interested in two types of non-stationary noise that may be present during test, namely slowly varying and abruptly varying noises. The context of our work is the Stochastic Matching framework. The Stochastic Matching compensation method transforms test data using an affine compensation function whose parameters are computed off-line. Stochastic Matching approaches are interesting since they make little assumptions about the nature and the level of the noise but they are best suited for the compensation of stationary noise. In this paper we propose an original contribution to the Stochastic Matching framework. It is based on an on-line frame-synchronous version of Stochastic Matching method to compensate for slowly varying noise. Our contribution extends this compensation algorithm in order to compensate for abruptly varying noise. The basic idea of the proposed methods is to perform the compensation and the recognition steps at the same time. The environment changes are identified using monitoring algorithms. The performance of our proposed methods is evaluated on two speech databases, one recorded in moving cars (VODIS), and another one obtained by corrupting VODIS with abruptly varying noise from NOISEX. The proposed approaches significantly outperform classical compensation methods (Off-line Stochastic Matching, Sequential Mean Cep-strum Removal, Parallel Model Combination, Spectral Subtraction). For instance, we obtain up to 32.6% word error rate reduction over S-MCR on database corrupted by a 10 dB abruptly varying white noise.
机译：当训练和测试条件不匹配时，本文讨论了语音识别中的噪声补偿问题。我们对测试期间可能出现的两种非平稳噪声感兴趣，即缓慢变化的噪声和突然变化的噪声。我们的工作环境是随机匹配框架。随机匹配补偿方法使用仿射补偿函数来转换测试数据，该仿射补偿函数的参数是离线计算的。随机匹配方法很有趣，因为它们几乎没有关于噪声的性质和水平的假设，但它们最适合于平稳噪声的补偿。在本文中，我们提出了对随机匹配框架的原始贡献。它基于随机匹配方法的在线帧同步版本，以补偿缓慢变化的噪声。我们的贡献扩展了该补偿算法，以补偿突然变化的噪声。所提出方法的基本思想是同时执行补偿和识别步骤。使用监视算法识别环境变化。我们在两种语音数据库上评估了我们提出的方法的性能，其中一种记录在行驶中的汽车（VODIS）上，另一种是通过NOISEX突然变化的噪声破坏VODIS而获得的。所提出的方法明显优于传统的补偿方法（离线随机匹配，顺序均值倒频谱去除，并行模型组合，谱减法）。例如，在被10 dB突然变化的白噪声破坏的数据库上，与S-MCR相比，我们获得了高达32.6％的字错误率降低。

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