System combination for out-of-vocabulary word detection

Long Qin, Ming Sun, Alexander I. Rudnicky

Abstract: This paper presents a method to improve the out-of-vocabulary (OOV) word detection performance by combining multiple speech recognition systems' outputs. Three different fragment-word hybrid systems, the phone, subword, and graphone systems, were built for detecting OOV words. Then outputs from each individual system were combined using ROVER. Two combination metrics were explored in ROVER, voting by word frequency and voting by both word frequency and word confidence score. The experimental results show that the OOV word detection performance of the ROVER system with confidence scores is better than the ROVER system with only word frequency, as well as any of the individual hybrid systems.