Named entity recognition from Conversational Telephone Speech leveraging Word Confusion Networks for training and recognition

Named Entity (NE) recognition from the results of Automatic Speech Recognition (ASR) is challenging because of ASR errors. To detect NEs, one of the options is to use a statistical NE model that is usually trained with ASR one-best results. In order to make NE recognition more robust to ASR errors, we propose using Word Confusion Networks (WCNs), sequences of bundled words, for both NE modeling and recognition by regarding the word bundles as units instead of the independent words. This is done by clustering similar word bundles that may originate from the same word. We trained the NE models with the maximum entropy principle and evaluated the performance using real-life call-center data. The results showed that by using the WCNs, the error of NE recognition was relatively reduced by up to 33.0%.