Lecture speech summarization based on the key sentence extraction using prosodic changes

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In order to investigate the efficiency of a stochastic FO model based on the clustering of accentual phrases, the accent type of accentual phrases in spoken sentences was estimated by the observed FO pattern and the model. Knowledge on accentual rules is introduced to reduce the potential accent type variations. The model was trained with 152 spoken lectures, and it was evaluated for other 15 spoken lectures in CSJ (Corpus of Spontaneous Japanese). It is shown that the introduction of accentual rules improves the performance of accent type estimation and the stochastic FO model is effectively constructed.Since speech data is not appropriate for quick scanning, the development of automatic summarization of lecture speech is expected. To realize the automatic summarization, the extraction of important sentences or words from speech data by hand was carried out. 17 subjects were asked to summarize 20 speech data from CSJ. It is confirmed that subjects are easy to agree on extracted key sentences for spoken lectures which have large prosodic inflections, based on the analysis of the agreement of extracted key sentences and the correlation between the extracted key sentences and FO variance.The relationship between the importance degree and prosodic parameters is analyzed for utterance units which are automatically segmented by more than 200ms pauses. The importance degree of utterance units is defined as the ratio that the unit is extracted as an important utterance by subjects. Although large correlation between the importance degree and prosodic parameters is not found, smoothing operation for several utterance units increased the correlation coefficients for power parameters. 5 utterance smoothing gives the largest correlation. Larger correlation as found for the duration parameter than other prosodic parameters.