An HMM/DNN framework is proposed to address the issues of short-word detection. The first-stage keyword hypothesizer is redesigned with a context-aware keyword model and a 9-state filler model to reduce the miss rate from 80% to 6% and increase the figure-of-merit (FOM) from 6.08% to 21.88% for short words. The hypothesizer is followed by a MLP-based second-stage keyword verifier to further reduce its putative hits. To enhance short word detection, three new techniques, including an HMM-based feature transformation for the MLPs, knowledge-based features, and deep neural networks, are incorporated into redesigning the verifier. With a set of nine short keywords from the TIMIT set the best FOM we had achieved for the proposed KWS system was 42.79%, which is comparable with that of 42.6% for long content words and much better than the FOM of 18.4% for short keywords reported in previous research.
Bibliographic reference. Chen, I-Fan / Lee, Chin-Hui (2013): "A hybrid HMM/DNN approach to keyword spotting of short words", In INTERSPEECH-2013, 1574-1578.