| 主要成果： |
主要论著：
[1]Yong Lü, Zhenyang Wu, Maximum likelihood subband polynomial regression for robust speech recognition, Applied Acoustics, 74 (5), 640–646, 2013. [2]Yong Lü, Haiyang Wu, Lin Zhou, Zhenyang Wu, Multi-environment model adaptation based on vector Taylor series for robust speech recognition, Pattern Recognition, 43(9), 3093-3099, 2010. [3]Yong Lü, Haiyang Wu and Zhenyang Wu, Robust speech recognition using improved vector Taylor series algorithm for embedded systems, IEEE Transactions on Consumer Electronics, 56(2), 764-769, 2010. [4]吕勇, 吴镇扬, 基于矢量泰勒级数的模型自适应算法, 电子与信息学报, 32(1), 107-111, 2010. [5]吕勇, 吴镇扬, 基于最大似然多项式回归的鲁棒语音识别, 声学学报, 35(1), 88-96, 2010. [6]吕勇, 吴镇扬, 基于矢量泰勒级数的鲁棒语音识别, 天津大学学报, 44(3), 261-265, 2011. [7]吕勇, 吴镇扬, 基于最大似然子带线性回归的鲁棒语音识别, 信号处理, 26(1), 74-79, 2010. [8]Yong Lü, Lin Zhou, Model adaptation based on improved variance estimation for robust speech recognition, 2012 International Conference on Wireless Communications and Signal Processing WCSP 2012. [9]Yong Lü, Lin Zhou, Model Adaptation Algorithm Based on Central Subband Regression for Robust Speech Recognition, 2014 7th International Symposium on Computational Intelligence and Design, 2014. [10]Yong Lü, Lin Zhou, Log-Spectral Linear Regression Based on Voicing Cut-Off Frequency for Robust Speech Recognition, 2015 8h International Symposium on Computational Intelligence and Design, 2015.
发明专利： [1]一种用于语音识别系统的多环境特征补偿方法 ZL201210488431.8. [2]语音识别系统中基于快速噪声估计的特征补偿方法 ZL201210486936.0. [3]一种基于声学模型阵列的鲁棒语音识别方法ZL201410699802.6. [4]一种用于语音识别系统的中心子带模型自适应方法ZL201410695733.1
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