Spontaneous speech consolidation for spoken language applications

Abstract

This paper describes the work done as a part of the International Workshop on Speech Summarization for Information Extraction and Machine Translation (IWSpS) , on spoken language processing including summarization, machine translation and question answering on lecture speech in the Translanguage English Database (TED) corpus . The hypotheses of lecture speech obtained by automatic speech recognition (ASR) system are ill-formed due to the spontaneity of speakers and recognition errors. The overall performance of spoken language processing components is affected by the errors introduced by the ASR system. In order to get more reliable phrases which maintain the original meaning and contribute positively to the total performance of the spoken language system, this paper proposes a consolidation fram ework. The consolidation approach extracts words by excluding redundant and irrelevant information and concatenating words so as to maintain the original meaning. Automatic consolidation performance is evaluated by comparing with manual consolidation by humans using a word accuracy metric . Our approach gives 58% accuracy on ASR output with 70% word accuracy.