Using a large vocabulary continuous speech recognizer for a constrained domain with limited training

How to train a speech recognizer with limited amount of training data is of interest to many researcher. We describe how we use BBN´s Byblos large vocabulary continuous speech recognition (LVCSR) system for the military air-traffic-control domain where we have less than an hour of training data. We investigate three ways to deal with the limited training data: (1) re-configure the LVCSR system to use fewer parameters, (2) incorporate out-of-domain data, and, (3) use pragmatic information, such as speaker identity and controller function to improve recognition performance. We compare the LVCSR performance to that of the tied-mixture recognizer that is designed for a limited vocabulary. We show that the reconfigured LVCSR system outperforms the tied-mixture system by 10% in absolute word error rate. When enough data is available per speaker, vocal tract length normalization and supervised adaptation techniques can further improve performance by 6% even for this domain with limited training. We also show that the use of out-of-domain data and pragmatic information, if available, can each further improve performance by 1-3%