A usage of the syllable unit based on morphological statistics in Korean large vocabulary continuous speech recognition system

In large vocabulary continuous speech recognition (LVCSR), it is important in improving the system’s performance to determine reasonably the recognition unit. In Korean continuous speech recognition, a morph rather than a word is used basically as the recognition unit due to Korean’s agglutinative property and a good performance is provided by combining high-frequency morph sequences, which leading to an increase of vocabulary size and high out-of-vocabulary (OOV) rate. Sub-lexical units such as a syllable and a graphone are widely used for inflectional languages, while they have not been introduced successfully for Korean speech recognition, due to a weakness of their linguistic information. In this paper, we investigate a usage of a syllable unit to resolve a mismatch problem between the recognition unit and vocabulary size that have occurred frequently in Korean large vocabulary speech recognition. We apply the local segmentation into syllables based on morphological statistics and perform experiments using the language model (LM) constructed from mixed unit types of morpheme, combined morpheme and syllable. By the proposed model, an absolute reduction of around 0.4% in word error rate (WER) is obtained compared to a traditional LM consisting of morphemes and combined morphemes.