Hidden Logistic Linear Regression for Support Vector Machine based Phone Verification

机译：基于支持向量机的电话验证的隐藏Logistic线性回归

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Phone verification approach to mispronunciation detection using a combination of Neural Network (NN) and Support Vector Machine (SVM) has been shown to yield improved verification performance. This approach uses a NN to predict the HMM state posterior probabilities. The average posterior probability vectors computed over each phone segment are used as input features to a SVM back-end to generate the final verification scores. In this paper, a novel Hidden Logistic Feature (HLF) for SVM back-end is proposed, where the sigmoid activations from the hidden layer that contain rich information of the NN is used instead of the output layer and the generation of HLFs can be interpreted as a Hidden Logistic Linear Regression process. Experiments on the TIMIT database show that the proposed HLF gives the lowest Equal Error Rate of 3.63%.

机译：结合使用神经网络（NN）和支持向量机（SVM）的电话确认错误发音检测方法已显示出改进的验证性能。这种方法使用NN来预测HMM状态的后验概率。在每个电话段上计算的平均后验概率矢量用作SVM后端的输入功能，以生成最终的验证分数。本文提出了一种新颖的SVM后端隐藏逻辑特征（HLF），其中使用来自隐层的包含NN丰富信息的S型激活代替输出层，从而可以解释HLF的产生作为隐藏的Logistic线性回归过程。 TIMIT数据库上的实验表明，所提出的HLF给出的最低均等错误率为3.63％。

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《Annual conference of the International Speech Communication Association;INTERSPEECH 2010》|2011年|p.2622-2625|共4页
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Bo Li; Khe Chai Sim;
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中图分类通信;
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phone verification; neural network; support vector machine; logistic linear regression;

机译：电话验证;神经网络;支持向量机逻辑线性回归;

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Hidden Logistic Linear Regression for Support Vector Machine based Phone Verification

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