Uncertainty Measures for Improving Exemplar-Based Source Separation
Uncertainty Measures for Improving Exemplar-Based Source Separation
复制标题
改进基于范例的源分离的不确定性措施
DOI：
--
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
K. Palomäki
中科院分区：
文献类型：
--
作者：
Heikki Kallasjoki;Ulpu Remes;J. Gemmeke;T. Virtanen;K. Palomäki
这项工作研究了使用观察不确定度来提高基于样本的基于源分离的前端语音识别性能。为了生成增强特征的观测不确定性估计，我们提出在基于样本的信源分离算法中使用基于噪声信号稀疏表示的启发式方法。在一个大词汇量噪声语音识别任务中对所提方法的有效性进行了评价。在没有不确定性测量的情况下，与基线特征增强方法相比，提出的最佳测量方法的相对误差降低了18%。索引术语：鲁棒性，语音识别，源分离，观察不确定性
This work studies the use of observation uncertainty measures for improving the speech recognition performance of an exemplar-based source separation based front end. To generate the observation uncertainty estimates for the enhanced features, we propose the use of heuristic methods based on the sparse representation of the noisy signal in the exemplar-based source separation algorithm. The effectiveness of the proposed measures is evaluated in a large vocabulary noisy speech recognition task. The best proposed measure achieved relative error reductions up to 18 % over the baseline feature enhancement method without uncertainty measures. Index Terms: robustness, speech recognition, source separation, observation uncertainties