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A Hybrid Genetic-Neural Front-End Extension for Robust Speech Recognition over TelephoneLines

机译:用于电话线上鲁棒语音识别的混合遗传神经前端扩展

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摘要

This paper presents a hybrid technique combining the Karhonen-Loeve Transform (KLT), the Multilayer Perceptron (MLP) and Genetic Algorithms (GAs) to obtain less-variant Mel-frequency parameters. The advantages of such an approach are that the robustness can be reached without modifying the recognition system, and that neither assumption nor estimation of the noise are required. To evaluate the effectiveness of the proposed approach, an extensive set of continuous speech recognition experiments are carried out by using the NTIMIT telephone speech database. The results show that the proposed approach outperforms the baseline and conventional systems.
机译:本文提出了一种混合技术,结合了Karhonen-Loeve变换(KLT),多层感知器(MLP)和遗传算法(GAs)来获得变化较小的Mel频率参数。这种方法的优点是无需修改识别系统就可以达到鲁棒性,并且不需要假设或估计噪声。为了评估该方法的有效性,使用NTIMIT电话语音数据库进行了广泛的连续语音识别实验。结果表明,所提出的方法优于基线和常规系统。

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