Improving Korean LVCSR with Long-Time Temporal Patterns and an Extended Phoneme Set

Abstract

Korean is an agglutinative language, in which pronunciations are affected by long-term context. In this paper, the long-time temporal information is investigated to improve Korean LVCSR. TRAP-based MLP features, which are able to utilize the scattered acoustic information over several hundred milliseconds, are employed to obtain additional information besides the conventional cepstral features. In contrast to the traditional Korean phoneme set, in which consonants in the initial and final positions are taken as the same, a more specific phoneme set is constructed via taking consonants as position dependent. In the Korean broadcast news speech recognition task, experiments show that with these improvements the character error rate has been reduced by 25.3% relatively over the baseline system.