利用句法和混淆网络结构进行词汇外词检测

2012 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2012-12-01 DOI:10.1109/SLT.2012.6424215

Alex Marin, T. Kwiatkowski, Mari Ostendorf, Luke Zettlemoyer

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引用次数: 21

摘要

本文研究了语音识别系统的词汇外检测问题，以提高语音自动翻译水平。检测系统利用给出混淆网络表示的置信度预测技术，并使用OOV单词令牌进行解析，以识别与真正的OOV单词相关的范围。在资源受限的领域中，我们实现了OOV检测f -得分为60-66，相对于未检测到OOV单词的情况，我们将单词错误率降低了12%。

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Using syntactic and confusion network structure for out-of-vocabulary word detection

This paper addresses the problem of detecting words that are out-of-vocabulary (OOV) for a speech recognition system to improve automatic speech translation. The detection system leverages confidence prediction techniques given a confusion network representation and parsing with OOV word tokens to identify spans associated with true OOV words. Working in a resource-constrained domain, we achieve OOV detection F-scores of 60-66 and reduce word error rate by 12% relative to the case where OOV words are not detected.

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2012 IEEE Spoken Language Technology Workshop (SLT)

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