Title: A Novel Real-Time Speech Summarizer System for the Learning of Sustainability
Authors: Wang, Hsiu-Wen
Cheng, Ding-Yuan
Chen, Chi-Hua
Wu, Yu-Rou
Lo, Chi-Chun
Lin, Hui-Fei
資訊管理與財務金融系 註:原資管所+財金所
Department of Communication and Technology
Department of Information Management and Finance
Issue Date: 1-Jan-2015
Abstract: As the number of speech and video documents increases on the Internet and portable devices proliferate, speech summarization becomes increasingly essential. Relevant research in this domain has typically focused on broadcasts and news; however, the automatic summarization methods used in the past may not apply to other speech domains (e.g., speech in lectures). Therefore, this study explores the lecture speech domain. The features used in previous research were analyzed and suitable features were selected following experimentation; subsequently, a three-phase real-time speech summarizer for the learning of sustainability (RTSSLS) was proposed. Phase One involved selecting independent features (e.g., centrality, resemblance to the title, sentence length, term frequency, and thematic words) and calculating the independent feature scores; Phase Two involved calculating the dependent features, such as the position compared with the independent feature scores; and Phase Three involved comparing these feature scores to obtain weighted averages of the function-scores, determine the highest-scoring sentence, and provide a summary. In practical results, the accuracies of macro-average and micro-average for the RTSSLS were 70% and 73%, respectively. Therefore, using a RTSSLS can enable users to acquire key speech information for the learning of sustainability.
URI: http://dx.doi.org/10.3390/su7043885
ISSN: 2071-1050
DOI: 10.3390/su7043885
Begin Page: 3885
End Page: 3899
Appears in Collections:Articles