We aim to improve spoken term detection performance by incorporating contextual information beyond traditional N-gram language models. Instead of taking a broad view of topic context in spoken documents, variability of word co-occurrence statistics across corpora leads us to focus instead the on phenomenon of word repetition within single documents. We show that given the detection of one instance 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 confident keyword hypothesis in each document and interpolating with lower scoring hits. We then develop a principled approach to select interpolation weights using only the ASR training data. Using this re-weighting approach we demonstrate consistent improvement in the term detection performance across all five languages in the BABEL program.
展开▼
