Context-based Speech Recognition Error Detection and Correction
Context-based Speech Recognition Error Detection and Correction
复制标题
基于上下文的语音识别错误检测和纠正
DOI：
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发表时间：
2004
期刊：
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通讯作者：
D. Palmer
中科院分区：
文献类型：
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作者：
A. Sarma;D. Palmer
在本文中，我们提出了一种新的无监督的方法，高精度的检测和纠正错误的自动语音识别系统的输出的初步结果。我们模拟ASR系统词汇表中所有单词的可能上下文，通过使用语音系统输出的大型语料库进行词汇共现分析。然后，我们识别数据中包含给定查询词的可能上下文的区域。最后，我们在上下文区域中检测单词或单词序列，这些单词或单词序列不太可能出现在上下文中并且在语音上与查询单词相似。初步实验表明，该技术可以产生高精度的有针对性的检测和纠正误识别的查询词。
In this paper we present preliminary results of a novel unsupervised approach for high-precision detection and correction of errors in the output of automatic speech recognition systems. We model the likely contexts of all words in an ASR system vocabulary by performing a lexical co-occurrence analysis using a large corpus of output from the speech system. We then identify regions in the data that contain likely contexts for a given query word. Finally, we detect words or sequences of words in the contextual regions that are unlikely to appear in the context and that are phonetically similar to the query word. Initial experiments indicate that this technique can produce high-precision targeted detection and correction of misrecognized query words.