15th Annual Conference of the International Speech Communication Association

September 14-18, 2014

Low-Resource Open Vocabulary Keyword Search Using Point Process Models

Chunxi Liu, Aren Jansen, Guoguo Chen, Keith Kintzley, Jan Trmal, Sanjeev Khudanpur

Johns Hopkins University, USA

The point process model (PPM) for keyword search is a whole-word parametric modeling framework based on the timing of phonetic events rather than the evolution of frame-level phonetic likelihoods. Recent progress in PPM training and decoding algorithms has yielded state-of-the-art phonetic search performance in high-resource settings, both in terms of accuracy and computational efficiency. In this paper, we consider PPM application to low-resource settings where the amount of transcribed speech is severely limited and the pronunciation dictionary is incomplete. By using (i) state-of-the-art deep neural network acoustic models to generate phonetic events and (ii) grapheme-to-phoneme conversion to generate pronunciations for out-of-vocabulary (OOV) keywords, we find the PPM system reaches state-of-the-art OOV search performance at a small computational cost. Moreover, due to their complementary methodologies, combining PPM outputs with the LVCSR baseline produces average relative ATWV improvements of 7% and 50% for in-vocabulary and OOV keywords, respectively (16% overall).

Full Paper

Bibliographic reference.  Liu, Chunxi / Jansen, Aren / Chen, Guoguo / Kintzley, Keith / Trmal, Jan / Khudanpur, Sanjeev (2014): "Low-resource open vocabulary keyword search using point process models", In INTERSPEECH-2014, 2789-2793.