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Neural networks for proper name retrieval in the framework of automatic speech recognition

机译:用于自动语音识别框架中正确名称检索的神经网络

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The problem of out-of-vocabulary words, more precisely proper names retrieval for in speech recognition is investigated. Speech recognition vocabulary is extended using diachronic documents. This article explores a new method based on neural network (NN), proposed recently by Mikolov. The NN uses high-quality continuous representation of words from large amounts of unstructured text data and predicts surrounding words of one input word. Different strategies of using the NN to take into account lexical context are proposed. Experimental results on broadcast speech recognition and comparison with previously proposed methods show an ability of NN representation to model semantic and lexical context of proper names.
机译:研究了词汇外词的问题,更准确地在语音识别中检索的正确名称。语音识别词汇量使用仪式文件扩展。本文探讨了基于神经网络(NN)的新方法,最近由Mikolov提出。 NN使用大量非结构化文本数据的高质量连续表示,并预测一个输入字的周围的单词。提出了使用NN考虑词汇背景的不同策略。与以前提出的方法的广播语音识别和比较的实验结果表明,NN表示对适当名称的模型语义和词汇背景的能力。

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