TR01：时间连续稀疏插补
TR01: Time-continuous Sparse Imputation
摘要：
提高自动语音识别抗噪声能力的一种有效方法是在解码之前将含噪语音特征标记为可靠或不可靠（缺失），并用干净的语音估计来替换缺失的特征。我们提出了一种新的方法来获得这样干净的语音估计。与以前的基于逐帧工作的估算框架不同，我们的方法侧重于从大的时间背景中利用信息。使用滑动窗口的方法，去噪的语音表示使用稀疏表示的可靠的功能在一个过完整的基础上的固定长度的样本片段。我们证明了我们的方法的潜力与实验上的AURORA-2连接数字数据库。
英文摘要：
An effective way to increase the noise robustness of automatic speech recognition is to label noisy speech features as either reliable or unreliable (missing) prior to decoding, and to replace the missing ones by clean speech estimates. We present a novel method to obtain such clean speech estimates. Unlike previous imputation frameworks which work on a frame-by-frame basis, our method focuses on exploiting information from a large time-context. Using a sliding window approach, denoised speech representations are constructed using a sparse representation of the reliable features in an overcomplete basis of fixed-length exemplar fragments. We demonstrate the potential of our approach with experiments on the AURORA-2 connected digit database.
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