Inference algorithms for generative score-spaces
Inference algorithms for generative score-spaces
复制标题
生成分数空间的推理算法
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
M. Gales
中科院分区：
文献类型：
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作者：
A. Ragni;M. Gales
使用例如隐马尔可夫模型(HMM)的生成模型来导出用于区分分类器的特征具有许多优点，包括使特征对说话人和噪声变化具有鲁棒性的能力。派生特征的一个有趣属性是，它们可能不具有与基本生成模型相同的条件独立性假设，后者通常是一阶马尔可夫模型。为了提高效率，在给定特定分段的情况下导出这些特征。本文描述了一种利用产生式和判别式相结合的模型来获得最优分割的通用算法。在特征被约束为具有一阶马尔可夫依赖的情况下，先前的结果被扩展以允许使用本质上非马尔可夫的派生特征。作为例子，考虑了零和一阶HMM得分空间的推理。给出了一个噪声污染的连续数字串识别任务：Aurora 2的实验结果。
Using generative models, for example hidden Markov models (HMM), to derive features for a discriminative classifier has a number of advantages including the ability to make the features robust to speaker and noise changes. An interesting attribute of the derived features is that they may not have the same conditional independence assumptions as the underlying generative models, which are typically first-order Markovian. For efficiency these features are derived given a particular segmentation. This paper describes a general algorithm for obtaining the optimal segmentation with combined generative and discriminative models. Previous results, where the features were constrained to have first-order Markovian dependencies, are extended to allow derivative features to be used which are non-Markovian in nature. As an example, inference with zero and first-order HMM score-spaces is considered. Experimental results are presented on a noise-corrupted continuous digit string recognition task: AURORA 2.