Paper: Can You Repeat That? Using Word Repetition to Improve Spoken Term Detection

ACL ID P14-1124
Title Can You Repeat That? Using Word Repetition to Improve Spoken Term Detection
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

We aim to improve spoken term detec- tion performance by incorporating con- textual information beyond traditional N- gram language models. Instead of taking a broad view of topic context in spoken doc- uments, variability of word co-occurrence statistics across corpora leads us to fo- cus instead the on phenomenon of word repetition within single documents. We show that given the detection of one in- stance of a term we are more likely to find additional instances of that term in the same document. We leverage this bursti- ness of keywords by taking the most con- fident keyword hypothesis in each docu- ment and interpolating with lower scor- ing hits. We then develop a principled approach to select interpolation weights using only the ASR training data. Us- ing this re-weighting approach ...