Vocabulary learning and environment normalization in vocabulary-independent speech recognition

The authors discuss adaptation issues of vocabulary-independent (VI) systems. Just as with speaker-adaptation in a speaker-independent system, two vocabulary learning algorithms are implemented in order to tailor the VI subword models to the target vocabulary. The first algorithm generates 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, gives the relevant allophones more prominence by assigning 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, codebook-dependent cepstral normalization (CDCN) and interpolated SNR-dependent cepstral normalization (ISDCN) originally designed for microphone adaptation are incorporated into the VI system, and both reduce the degradation of VI cross-environment recognition by 50%