ISCA Archive Interspeech 2013
ISCA Archive Interspeech 2013

A hybrid HMM/DNN approach to keyword spotting of short words

I-Fan Chen, Chin-Hui Lee

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.


doi: 10.21437/Interspeech.2013-397

Cite as: Chen, I.-F., Lee, C.-H. (2013) A hybrid HMM/DNN approach to keyword spotting of short words. Proc. Interspeech 2013, 1574-1578, doi: 10.21437/Interspeech.2013-397

@inproceedings{chen13d_interspeech,
  author={I-Fan Chen and Chin-Hui Lee},
  title={{A hybrid HMM/DNN approach to keyword spotting of short words}},
  year=2013,
  booktitle={Proc. Interspeech 2013},
  pages={1574--1578},
  doi={10.21437/Interspeech.2013-397}
}