Ieee Journal of Selected Topics in Signal Processing 1 Compressive Sensing for Missing Data Imputation in Noise Robust Speech Recognition
Ieee Journal of Selected Topics in Signal Processing 1 Compressive Sensing for Missing Data Imputation in Noise Robust Speech Recognition
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通讯作者：
Jort Florent Gemmeke;Hugo Van Hamme;B. Cranen;L. Boves
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作者：
Jort Florent Gemmeke;Hugo Van Hamme;B. Cranen;L. Boves
- 提高自动语音识别的噪声鲁棒性的有效方法是将噪声语音特征标记为可靠或不可靠（缺失），并通过干净的语音估计来替换（估算）缺失的特征。传统的插补技术采用参数模型并逐帧插补缺失的特征。在低信噪比下，这些技术会失败，因为太多的时间帧可能包含很少（如果有的话）可靠的特征。在本文中，我们基于压缩感知领域的技术，介绍了一种新颖的非参数、基于样本的方法，用于从噪声观测中重建干净的语音。该方法被称为稀疏插补，可以使用更大的时间窗口（例如整个单词）来插补缺失的特征。该方法使用干净语音样本的超完备字典，找到最稀疏的样本组合，共同近似噪声话语的可靠特征。干净语音样本的线性组合用于替换缺失的特征。对噪声隔离数字的识别实验表明，当使用理想的“oracle”掩模时，稀疏插补在 SNR = −5 dB 时优于传统插补技术。对于容易出错的估计掩模，稀疏插补的性能比最佳传统技术稍差。
—An effective way to increase the noise robustness of automatic speech recognition is to label noisy speech features as either reliable or unreliable (missing), and to replace (impute) the missing ones by clean speech estimates. Conventional im-putation techniques employ parametric models and impute the missing features on a frame-by-frame basis. At low SNR's these techniques fail, because too many time frames may contain few, if any, reliable features. In this paper we introduce a novel non-parametric, exemplar-based method for reconstructing clean speech from noisy observations , based on techniques from the field of Compressive Sensing. The method, dubbed sparse imputation, can impute missing features using larger time windows such as entire words. Using an overcomplete dictionary of clean speech exemplars, the method finds the sparsest combination of exemplars that jointly approximate the reliable features of a noisy utterance. That linear combination of clean speech exemplars is used to replace the missing features. Recognition experiments on noisy isolated digits show that sparse imputation outperforms conventional imputation techniques at SNR = −5 dB when using an ideal 'oracle' mask. With error-prone estimated masks sparse imputation performs slightly worse than the best conventional technique.