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区分性模型组合中基于决策树的声学上下文建模方法

DOI: 10.3724/SP.J.1004.2012.01449, PP. 1449-1458

Keywords: 区分性模型组合,上下文建模,声学决策树,最小音子错误,语音识别

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Abstract:

?上下文相关的区分性模型组合的局限在于引入大的模型权重参数集,在数据有限时容易导致区分性权重训练过拟合.针对该问题,本文提出利用决策树进行上下文建模,采用最小音子错误准则构建决策树以获得最优上下文相关权重参数集.决策树构造过程中通过评估目标函数的一阶近似增量来加速最优问题集的选择,并利用精细问题集来获得更好的声学区分能力.基于多模型组合的语音识别实验表明,该方法能够增强权重训练对过拟合的鲁棒性,在大幅减小参数数量的情况下降低误识率,并优于在特征空间进行组合的方法.

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