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Korean Broadcast News Transcription Using Morpheme-based Recognition Units  

Kwon, Oh-Wook (Brain Science Research Center, KAIST)
Alex Waibel (Interactive Systems Laboratories, University of Karlsruhe)
Publication Information
The Journal of the Acoustical Society of Korea / v.21, no.1E, 2002 , pp. 3-11 More about this Journal
Abstract
Broadcast news transcription is one of the hardest tasks in speech recognition because broadcast speech signals have much variability in speech quality, channel and background conditions. We developed a Korean broadcast news speech recognizer. We used a morpheme-based dictionary and a language model to reduce the out-of·vocabulary (OOV) rate. We concatenated the original morpheme pairs of short length or high frequency in order to reduce insertion and deletion errors due to short morphemes. We used a lexicon with multiple pronunciations to reflect inter-morpheme pronunciation variations without severe modification of the search tree. By using the merged morpheme as recognition units, we achieved the OOV rate of 1.7% comparable to European languages with 64k vocabulary. We implemented a hidden Markov model-based recognizer with vocal tract length normalization and online speaker adaptation by maximum likelihood linear regression. Experimental results showed that the recognizer yielded 21.8% morpheme error rate for anchor speech and 31.6% for mostly noisy reporter speech.
Keywords
Continuous speech recognition; Broadcast news transcription; Language model; Morpheme-based speech recognition;
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