A topic independent lexical and language modeling for robust key-phrase detection and verification is presented. Instead of assuming a domain specific lexicon and language model, our model is designed to characterize filler phrases depending on the speaking-style, thus can be trained with large corpora of different topics but the same style. Mutual information criterion is used to select topic independent filler words and their N-gram model is used for verification of key-phrase hypotheses. A dialogue-style dependent filler model improves the key-phrase detection in different dialogue applications. A lecture-style dependent model is trained with transcriptions of various oral presentations by filtering out topic specific words. It performs much better verification of key-phrases uttered during lectures of different topics compared with the conventional syllable-based model and large vocabulary model.
['Tatsuya Kawahara', 'Shuji Doshita']
Topic independent language model for key-phrase detection and verification