ISCA Archive Interspeech 2014
ISCA Archive Interspeech 2014

Progress in the BBN keyword search system for the DARPA RATS program

Tim Ng, Roger Hsiao, Le Zhang, Damianos Karakos, Sri Harish Mallidi, Martin Karafiát, Karel Veselý, Igor Szőke, Bing Zhang, Long Nguyen, Richard Schwartz

This paper presents a set of techniques that we used to improve our keyword search system for the third phase of the DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state of the art detection capabilities on audio from highly degraded radio communication channels. The results for both Levantine and Farsi, which are the two target languages for the keyword search (KWS) task, are reported. About 13% absolute reduction in word error rate (from 70.2% to 57.6%) is achieved by using acoustic features derived from stacked Multi-Layer Perceptrons (MLP) and Deep Neural Network (DNN) acoustic models. In addition to score normalization and score/system combination for keyword search, we showed that the false alarm rate at the target false reject rate (15%) was reduced by about 1% (from 5.39% to 4.45%) by reducing the deletion errors of the speech-to-text system.

doi: 10.21437/Interspeech.2014-254

Cite as: Ng, T., Hsiao, R., Zhang, L., Karakos, D., Mallidi, S.H., Karafiát, M., Veselý, K., Szőke, I., Zhang, B., Nguyen, L., Schwartz, R. (2014) Progress in the BBN keyword search system for the DARPA RATS program. Proc. Interspeech 2014, 959-963, doi: 10.21437/Interspeech.2014-254

  author={Tim Ng and Roger Hsiao and Le Zhang and Damianos Karakos and Sri Harish Mallidi and Martin Karafiát and Karel Veselý and Igor Szőke and Bing Zhang and Long Nguyen and Richard Schwartz},
  title={{Progress in the BBN keyword search system for the DARPA RATS program}},
  booktitle={Proc. Interspeech 2014},