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首页> 外文会议>Asia-Pacific Signal and Information Processing Association Annual Summit and Conference >KL-divergence based mispronunciation detection via DNN and decision tree in the phonetic space
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KL-divergence based mispronunciation detection via DNN and decision tree in the phonetic space

机译：在语音空间中通过DNN和决策树进行基于KL散度的错读检测

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摘要

We propose to detect mispronunciations in a language learners speech via a discriminatively trained DNN in the phonetic space. The posterior probabilities of “senones” populated in a decision tree are trained and predicted speaker independently. Acoustic features of each input segment (with preceding and succeeding contexts of several frames) are mapped unto the whole set of senones in their corresponding posteriors. Vectors of senone posteriors are used as stochastic characterization of input speech segments in the phonetic space. Distortion between any two such vectors are measured with the symmetric Kullback-Leibler Divergence (KLD) and they are used for performing vector clustering and computing the corresponding centroids in a phonetically oriented senone based decision tree. Experimental results, tested on a large, Mandarin database (iCALL) of L2 language learners, show that the proposed approach to mispronunciation detection can achieve a 3.0% of equal precision and recall improvement over our best DNN-based, Goodness of Pronunciation (GOP) baseline system with adaptation. When the original maximum-likelihood trained decision tree is retrained with the symmetric KLD measure, further improvement of 0.8% of equal precision and recall can be obtained.
机译：我们建议通过在语音空间中经过严格训练的DNN来检测语言学习者语音中的错误发音。决策树中填充的“ senones”的后验概率被独立地训练和预测。每个输入段的声音特征（具有几帧的前后上下文）被映射到其相应后代中的整个senone集。 senone后验向量被用作语音空间中输入语音段的随机表征。使用对称的Kullback-Leibler发散（KLD）测量任意两个这样的向量之间的失真，并将它们用于执行向量聚类并计算基于语音的基于senone的决策树中的相应质心。在L2语言学习者的大型普通话数据库（iCALL）上进行的实验结果表明，与我们基于DNN的最佳语音发音（GOP）相比，所提出的错误发音检测方法可以达到3.0％的相等精度，并可以提高召回率具有适应性的基线系统。当使用对称KLD度量对原始的最大似然训练决策树进行重新训练时，可以获得等精度和召回率的0.8％的进一步提高。

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