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

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 burstiness 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.