Improvements in automatic speech summarization and evaluation methods

This paper proposes an improved method of summarizing speech in which a con dence measure of a word hypothesis is incorporated in the summarization score and also proposes a new method for evaluating the summarized sentences. The automatically summarized sentences were evaluated based on the precision of extracted keywords and each word string with a certain length in the manual summarizations by human subjects. Japanese broadcast-news speech transcribed using a large-vocabulary continuousspeech recognition (LVCSR) system was summarized using our proposed method. Experimental results show that a con dence score giving a penalty for acoustically as well as linguistically unreliable hypotheses can reduce the meaning alteration of summarizations caused by recognition errors especially when the speech recognition rate is relatively low.