Featherweight phonetic keyword search for conversational speech

摘要

The point process model (PPM) for keyword search is a phonetic event-driven approach that provides a whole-word focused alternative to fast lattice matching techniques. Recent efforts in PPMs have been focused on improved model estimation techniques and efficient search algorithms, but past evaluations have been limited to searching relatively easy scripted corpora for simple unigram queries, preventing comprehensive benchmarking against standard search methods. In this paper, we present techniques for score normalization and the processing of multi-word and out of training query terms as required by the 2006 NIST Spoken Term Detection (STD) evaluation, permitting the first comprehensive benchmark of PPM search technology against state-of-the-art word and phonetic-based search systems. We demonstrate PPM to be the fastest phonetic system while posting accuracies competitive with the best phonetic alternatives. Moreover, index construction time and size are better than any keyword search system entered in the NIST evaluation.