System Combination for Spoken Language Understanding

One of the first steps in an SLU system usually is the extraction of fiat concepts. Within this paper, we present five methods for concept tagging and give experimental results on the state-of-the-art MEDIA corpus for both, manual transcriptions (REF) and ASR input (ASR). Compared to previous publications, some single systems could be improved and the ASR results are presented for the first time. We could improve the tagging performance of the best known result on this task by approx. 7% relatively from 16.2% to 15.0% CER for REF using light-weight system combination (ROVER). For the ASR task, we achieve improvements by approx. 3% relatively from 29.8% to 28.9% CER. An analysis of the differences in performance on both tasks is also given.