Rejection Performance Analysis in Vocabulary Independent Speech Recognition Based on Normalized Confidence Measure

Kim et al. proposed Normalized Confidence Measure (NCM) [1-2] and it was successfully used for rejecting mis-recognized words in isolated word recognition. However, their experiments were performed on the fixed word speech recognition. In this paper we apply NCM to the domain of vocabulary independent speech recognition (VISP) and shows the rejection performance of NCM in VISP. Specially we propose vector quantization (VQ) based method for overcoming the problem of unseen triphones. It is because NCM uses the statistics of triphone confidence in the case of triphone-based normalization. According to speech recognition experiments phone-based normalization method shows better results than RLJC [3] and also triphone-based normalization approach. This results are different with those of Kim et al [1-2]. Concludingly the phone-based normalization shows robust performance in VISP domain.