Spoken Language Recognition with Relevance Feedback

This paper applies relevance feedback technique in spoken language recognition task, in which we consider a test utterance as a test query. Assuming that we have a labeled multilingual corpus, we exploit the retrieved utterances from such a reference corpus to automatically augment the test query. Note that successful spoken language recognition relies on sufficient query data. The proposed method is especially effective for short query by expanding the query at a low cost. Experiments show that unsupervised relevance feedback reduces the relative equal-error-rate by 16.2%, 4.9% and 10.2% on NIST LRE 1996, 2003 and 2005 databases respectively for 3-second trials.