Using comparison of parallel phoneme probability streams for OOV word detection

In this paper, we investigate the approach of comparing two different parallel streams of phoneme posterior probability estimates for OOV word detection. The first phoneme posterior probability stream is estimated using only the knowledge of short-term acoustic observation. In our work we refer this stream as “out-of-context posteriors”. The second posterior probability stream, referred also as “in-context posteriors” is estimated using the knowledge of the whole acoustic observation sequence: the acoustic model and the language model of an ASR system. In particular, we focus our study on different types of distance measures, namely KL-divergence and Euclidean distance, to compare the two phoneme posterior probability streams. Our experiments on large vocabulary automatic speech recognition task shows that using KL-divergence measure estimated with the in-context posteriors as reference distribution consistently yields a better OOV word detection system.