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PSEUDO-MORPHEME AND CONFUSION NETWORK BASED KOREAN-ENGLISH STATISTICAL SPOKEN LANGUAGE TRANSLATION SYSTEM

In this demonstration, we present POSSLT (POSTECH Spoken Language Translation) for a Korean-English statistical spoken language translation (SLT) system using pseudo-morpheme and confusion network (CN) based technique. Like most other SLT systems, automatic speech recognition (ASR) and machine translation (MT) are coupled in a cascading manner in our SLT system. We used confusion network based approach to couple ASR and MT. It has better translation quality and faster decoding time than N-best approach. In the ASR and SMT for Korean, how to define processing units affects the performance. Pseudo-morpheme unit is a best choice for Korean-English SLT. Models used in SLT system are trained on a travel domain conversational corpus.