Vocabulary And Environment Adaptation In Vocabulary-Independent Speech Recognition


In this paper, we are looking into the adaptation issues of vocabulary-independent (VI) systems. Just as with speaker adaptation in speaker-independent system, two vocabulary adaptation algorithms [5] are implemented in order to tailor the VI subword models to the target vocabulary. The first algorithm is to generate vocabulary-adapted clustering decision trees by focusing on relevant allophones during tree generation and reduces the VI error rate by 9%. The second algorithm, vocabulary-bias training, is to give the relevant allophones more prominence by assign more weight to them
during Baum-Welch training of the generalized allophonic models and reduces the VI error rate by 15%. Finally, in order to overcome the degradation caused by the different acoustic environments used for VI training and testing, CDCN and ISDCN originally designed for microphone adaptation are incorporated into our VI system and both reduce the degradation of VI cross-environment recognition by 50%. 

