Boosting HMMs with an application to speech recognition
Boosting HMMs with an application to speech recognition
复制标题
通过语音识别应用增强 HMM
DOI：
10.1109/icassp.2004.1327187
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Samy Bengio
中科院分区：
文献类型：
--
作者：
Christos Dimitrakakis;Samy Bengio
增强是一种训练分类器集合的通用方法，其目的是相对于单个分类器提高性能。虽然最初的AdaBoost算法是为分类任务定义的，但目前的工作是研究它在序列学习问题上的适用性，重点是语音识别。我们在音素模型级别应用增强，并使用多流技术重新组合专家决策。
Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines its applicability to sequence learning problems, focusing on speech recognition. We apply boosting at the phoneme model level and recombine expert decisions using multi-stream techniques.