Learning Sub-Word Units for Open Vocabulary Speech Recognition
Carolina Parada, Mark H. Dredze, Abhinav Sethy, Ariya Rastrow · 2011
Large vocabulary speech recognition systems fail to recognize words beyond their vocabulary, many of which are information rich terms, like named entities or foreign words. Hybrid word/sub-word systems solve this problem by adding sub-word units to large vocabulary word based systems; new words can then be represented by combinations of subword units. Previous work heuristically created the sub-word lexicon from phonetic representations of text using simple statistics to select common phone sequences. We propose a probabilistic model to learn the subword lexicon optimized for a given task. We consider the task of out of vocabulary (OOV) word detection, which relies on output from a hybrid model. A hybrid model with our learned sub-word lexicon reduces error by 6.3 % and 7.6 % (absolute) at a 5 % false alarm rate on an English Broadcast News and MIT Lectures task respectively. 1