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.
Bibliographic reference. Ng, Tim / Hsiao, Roger / Zhang, Le / Karakos, Damianos / Mallidi, Sri Harish / Karafiát, Martin / Veselý, Karel / Szőke, Igor / Zhang, Bing / Nguyen, Long / Schwartz, Richard (2014): "Progress in the BBN keyword search system for the DARPA RATS program", In INTERSPEECH-2014, 959-963.