Leveraging Kullback–Leibler Divergence Measures and Information-Rich Cues for Speech Summarization
Shih-Hsiang Lin, Yao‐Ming Yeh, Berlin Chen · IEEE Transactions on Audio Speech and Language Processing · 2010
Imperfect speech recognition often leads to degraded performance when exploiting conventional text-based methods for speech summarization. To alleviate this problem, this paper investigates various ways to robustly represent the recognition hypotheses of spoken documents beyond the top scoring ones. Moreover, a summarization framework, building on the Kullback-Leibler (KL) divergence measure and exploring both the relevance and topical information cues of spoken documents and sentences, is presented to work with such robust representations. Experiments on broadcast news speech summarization tasks appear to demonstrate the utility of the presented approaches.