A rejection method for the Korean isolated word recognition system

Abstract

The small vocabulary isolated word recognition systems as well as large vocabulary continuous speech recognition systems can be applicable in many areas. For the isolated word recognition systems to be deployable in actual applications, the ability to reject the out-of-vocabulary is required. This paper presents a rejection method which uses the clustered phoneme modeling combined with postprocessing by likelihood ratio scoring. Our baseline speech recognition was based on the whole-word continuous HMM. Six clustered phoneme models were generated using the statistical method, monophone clustering algorithm, from the 45 context independent Korean phoneme models, which were trained using the phonetically balanced Korean speech database. The performance of this method was assessed in terms of the out-of-vocabulary rejection rate and the accuracy on the pre-defined-vocabulary. The performance test for speaker independent isolated words recognition task on the 22 section names shows that this method is superior to the conventional postprocessing method, which is performing rejection according to the likelihood difference between the first and second candidates. Furthermore, these clusters phoneme models do not require retraining for the other isolated word recognition systems with different vocabulary sets.