Improved phonotactic language identification using random forest language models

机译：使用随机森林语言模型改进的音符语言识别

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Recently a new language model, the random forest language model (RFLM), has been proposed and shown encouraging results in speech recognition tasks. In this paper we applied the RFLM to language identification tasks. We proposed a shared backoff smoothing

机译：最近，已经提出了一种新的语言模型，即随机森林语言模型（RFLM），并在语音识别任务中显示出令人鼓舞的结果。在本文中，我们将RFLM应用于语言识别任务。我们提出了共享退避平滑

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《Personal, Indoor and Mobile Radio Communications,2005 IEEE 16th International Symposium on》|2008年|P.4237-4240|共4页
会议地点 Las Vegas,NV
作者
XiaoRui Wang; ShiJin Wang; JiaEn Liang; Bo Xu;
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decision trees; natural language processing; speech recognition; decision tree; n-gram LM; phonotactic language identification; random forest language models; shared backoff smoothing; sparseness problem; speech recognition; system fusion; Decision Tree Language Mo;

机译：决策树；自然语言处理；语音识别;决策树; n-gram LM;语音语言识别;随机森林语言模型；共享退避平滑；稀疏问题语音识别;系统融合；决策树语言Mo;

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Improved phonotactic language identification using random forest language models

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