Speech summarization - Information extraction from speech
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Sadaoki Furui and Chiori Hori
Department of Computer Science
Tokyo Institute of Technology
2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8552 Japan
furui@cs.titech.ac.jp
For processing spontaneous speech, it is necessary to introduce a paradigm
shift from speech recognition to understanding. In this paragigm, underlying
messages of the speaker are extracted instead of transcribing all the spoken
words. As a step toward speech understanding, we have proposed a new method
of automatically summarizing speech by extracting a limited number of relatively important words from its automatic transcription according to a
target compression ratio for the number of characters. To determine a word
set to be extracted, we defined a summarization score consisting of a topic
score (significance measure) of words and a linguistic score (linguistic
likelihood) of the word concatenation. A set of words maximizing the score
is efficiently selected using a dynamic programming (DP) technique. This
method was applied to Japanese broadcast news speech and very encouraging
results were obtained. This talk also briefly introduces a new Japanese
national project entitled "Spontaneous Speech: Corpus and Processing
Technology".
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