In this paper, we propose a novel feature compensation algorithm based on independent noise estimation, which employs a Gaussian mixture model (GMM) with fewer Gaussian components to rapidly estimate the noise parameters from the noisy speech and monitor the noise variation. The estimated noise model is combined with a GMM with sufficient Gaussian mixtures to produce the noisy GMM for the clean speech estimation so that parameters are updated if and only if the noise variation occurs. Experimental results show that the proposed algorithm can achieve the recognition accuracy similar to that of the traditional GMM-based feature compensation, but significantly reduces the computational cost, and thereby is more useful for resource-limited mobile devices.

中文翻译：

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基于独立噪声估计的特征补偿用于鲁棒语音识别

在本文中，我们提出了一种基于独立噪声估计的新型特征补偿算法，该算法采用具有较少高斯分量的高斯混合模型（GMM）来快速估计噪声语音中的噪声参数并监测噪声变化。估计的噪声模型与具有足够高斯混合的 GMM 相结合，以产生用于干净语音估计的带噪 GMM，以便当且仅当发生噪声变化时才更新参数。实验结果表明，该算法可以达到与传统的基于GMM的特征补偿相似的识别精度，但显着降低了计算成本，从而更适用于资源有限的移动设备。