论文标题
使用扭曲的盲目反转来改善扬声器的识别
Speaker recognition improvement using blind inversion of distortions
论文作者
论文摘要
在本文中，我们提出了非线性畸变的反转，以提高说话者识别器系统的识别率。我们研究饱和度对测试信号的影响，试图考虑在受控情况下记录训练材料的实际情况，但是测试信号与输入信号水平（饱和度）呈现了一些不匹配。实验结果表明，具有和不具有非线性失真补偿的数据融合的组合可以通过饱和测试句子从80％提高识别率从80％提高到88.57％，而一个麦克风的饱和测试句子的结合量可以从80％到88.57％提高识别率。
In this paper we propose the inversion of nonlinear distortions in order to improve the recognition rates of a speaker recognizer system. We study the effect of saturations on the test signals, trying to take into account real situations where the training material has been recorded in a controlled situation but the testing signals present some mismatch with the input signal level (saturations). The experimental results shows that a combination of data fusion with and without nonlinear distortion compensation can improve the recognition rates with saturated test sentences from 80% to 88.57%, while the results with clean speech (without saturation) is 87.76% for one microphone.
