Selection criteria for hypothesis driven lexical adaptation

Adapting the vocabulary of a speech recognizer to the utterance to be recognized has proven to be successful both in reducing high out-of-vocabulary as well as word error rates. This applies especially to languages that have a rapid vocabulary growth due to a large number of inflections and composita. This paper presents various adaptation methods within the hypothesis driven lexical adaptation (HDLA) framework which allow speech recognition on a virtually unlimited vocabulary. Selection criteria for the adaptation process are either based on morphological knowledge or distance measures at phoneme or grapheme level. Different methods are introduced for determining distances between phoneme pairs and for creating the large fallback lexicon the adapted vocabulary is chosen from. HDLA reduces the out-of-vocabulary-rate by 55% for Serbo-Croatian, 35% for German and 27% for Turkish. The reduced out-of-vocabulary rate also decreases the word error rate by an absolute 4.1% to 25.4% on Serbo-Croatian broadcast news data.