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首页> 外文会议>European Conference on Speech Communication and Technology v.3; 20010903-20010907; Aalborg; DK >Using Boosting and POS Word Graph Tagging to Improve Speech Recognition
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Using Boosting and POS Word Graph Tagging to Improve Speech Recognition

机译：使用Boosting和POS字图标记来改善语音识别

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摘要

The word graphs produced by a large vocabulary speech recognition system usually contain a path labelled with the correct utterance, but this is not always the highest scoring path. Boosting increases the probability of words which occur often in the word graph, which are in some sense robust. Adding syntactic information allows rescoring of arc probabilities with the possibility that more grammatical word sequences will also be the correct ones. A theory is developed which allows general probabilistic syntactic models to be used to rescore word lattices. Experiments conducted on the Wall Street Journal (WSJ) corpus with a version of the AT&T 1995 FST LVSR system with part of speech (POS) trigram sequences show that using only POS leads to a loss in performance. Boosting alone provides an improvement in performance which is not statistically significant. Cascading the two methods, boosting first and then using syntactic information improves performance 4.5 % relative on a large portion of the 1995 DARPA test set.
机译：大型词汇语音识别系统生成的词图通常包含标有正确话语的路径，但这并不总是最高的得分路径。增强会增加单词图中经常出现的单词的概率，从某种意义上说，单词在某种程度上是健壮的。添加句法信息可以对弧形概率进行记录，并且更多的语法单词序列也将是正确的。发展了一种理论，该理论允许将一般的概率句法模型用于重排词格。在《华尔街日报》（WSJ）语料库上，使用部分语音（POS）三字母顺序的AT＆T 1995 FST LVSR系统进行的实验表明，仅使用POS会导致性能下降。单独增强可改善性能，但在统计上并不显着。相对于1995年DARPA测试集的大部分而言，将两种方法（首先增强然后使用语法信息）进行级联可以将性能提高4.5％。

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