Residual noise compensation for robust speech recognition in nonstationary noise
Residual noise compensation for robust speech recognition in nonstationary noise
复制标题
用于非平稳噪声中鲁棒语音识别的残余噪声补偿
DOI：
10.1109/icassp.2000.859162
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发表时间：
2000
期刊：
影响因子：
--
通讯作者：
Z. Cao
中科院分区：
文献类型：
--
作者：
K. Yao;Bertram E. Shi;Pascale Fung;Z. Cao
提出了一种基于模型的噪声补偿算法，用于非平稳噪声环境下的鲁棒语音识别。噪声的影响被分成一个固定的部分，通过并行模型组合补偿，和一个随时间变化的残差。残余噪声参数的演化由一组状态空间模型表示。状态空间模型采用卡尔曼预测和序贯最大似然算法进行更新。对来自不同混合物的残余噪声参数的预测进行融合，并且融合的噪声参数用于修改每个混合物的线性化似然得分。噪声补偿与识别并行进行。实验结果表明，该算法提高了识别性能在高度非平稳环境中，相比单独的并行模型组合。
We present a model-based noise compensation algorithm for robust speech recognition in nonstationary noisy environments. The effect of noise is split into a stationary part, compensated by parallel model combination, and a time varying residual. The evolution of residual noise parameters is represented by a set of state space models. The state space models are updated by Kalman prediction and the sequential maximum likelihood algorithm. Prediction of residual noise parameters from different mixtures are fused, and the fused noise parameters are used to modify the linearized likelihood score of each mixture. Noise compensation proceeds in parallel with recognition. Experimental results demonstrate that the proposed algorithm improves recognition performance in highly nonstationary environments, compared with parallel model combination alone.