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Comparison of several preprocessing techniques for robust speech recognition over both PSN and GSM networks

机译：几种预处理技术对PSN和GSM网络的鲁棒语音识别的预处理技术

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In this paper several preprocessing techniques used to improve speech recognition performance are compared over both PSN and GSM networks. Recognition experiments are conducted on a digit database in a speaker-independent isolated-word mode in order to evaluate the performances under within- and cross-network (PSN and GSM) conditions. Two classes of preprocessing techniques are distinguished depending on whether they deal with additive ambient noise or convolved perturbations. The first class preprocessing techniques are based on spectral subtraction. In the second class, the low frequencies of cepstral trajectories are eliminated in order to reduce convolved disturbances. Blind equalization adaptive filtering has been proposed to reduce channel effects. In this study, channel equalization and speech enhancement techniques are combined and compared. Different recording conditions may be integrated in order to increase robustness. This is done during the training phase using HMM models with variable parameters. Recognition results are analysed as a function of recording conditions.
机译：本文在PSN和GSM网络中比较了用于改进语音识别性能的几种预处理技术。识别实验在扬声器无关的隔离字模式下在数字数据库上进行，以便在内部和跨网络（PSN和GSM）条件下进行性能。根据它们是否处理添加剂环境噪声或卷积扰动，有两种类别的预处理技术。第一类预处理技术基于光谱减法。在第二类中，消除了抗搏汗轨迹的低频以减少卷曲干扰。已经提出了盲均衡自适应滤波以降低信道效应。在该研究中，组合并比较通道均衡和语音增强技术。可以集成不同的记录条件以增加鲁棒性。这是在训练阶段使用具有可变参数的HMM模型来完成的。作为记录条件的函数分析识别结果。

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