Experiments with an extended adaptive SVD enhancement scheme for speech recognition in noise
Experiments with an extended adaptive SVD enhancement scheme for speech recognition in noise
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用于噪声中语音识别的扩展自适应 SVD 增强方案的实验
DOI：
10.1109/icassp.2001.940822
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发表时间：
2001
期刊：
影响因子：
--
通讯作者：
M. Lieb
中科院分区：
文献类型：
--
作者：
C. Uhl;M. Lieb
提出了一种基于奇异值分解（SVD）的自适应信号子空间方法，并在线估计噪声方差。这种方法的目的是在不利的环境条件下的自动语音识别（ASR）没有语音检测必须执行。在弱相关噪声情况下，对不同应用的ASR实验中的不同SVD方法和非线性谱减法进行了比较。更好的性能的情况下，信号子空间语音增强的精度以及鲁棒性的参数调整的报告。
An extension to adaptive signal subspace methods is presented, based on singular value decomposition (SVD) with an online estimation of the noise variance. With this approach aiming at automatic speech recognition (ASR) in adverse environmental conditions no speech detection has to be performed. A comparison of different SVD approaches and nonlinear spectral subtraction within ASR experiments of different applications is conducted for weakly correlated noise scenarios. Better performance in the case of signal subspace speech enhancement with respect to both accuracy as well as robustness of parameter tuning are reported.