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This paper examines the relationship between dynamic spectral features and the identification of Japanese syllables modified by initial and/or final truncation. The experiments confirm several main points. "Perceptual critical points," where the percent correct identification of the truncated syllable as a function of the truncation position changes abruptly, are related to maximum spectral transition positions. A speech wave of approximately 10 ms in duration that includes the maximum spectral transition position bears the most important information for consonant and syllable perception. Consonant and vowel identification scores simultaneously change as a function of the truncation position in the short period, including the 10-ms period for final truncation. This suggests that crucial information for both vowel and consonant identification is contained across the same initial part of each syllable. The spectral transition is more crucial than unvoiced and buzz bar periods for consonant (syllable) perception, although the latter features are of some perceptual importance. Also, vowel nuclei are not necessary for either vowel or syllable perception.

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This paper proposes a new automatic speech summarization method. In this method, a set of words maximizing a summarization score is extracted from automatically transcribed speech. This extraction is performed according to a target compression ratio using a dynamic programming (DP) technique. The extracted set of words is then connected to build a summarization sentence. The summarization score consists of a word significance measure, a confidence measure, linguistic likelihood, and a word concatenation probability. The word concatenation score is determined by a dependency structure in the original speech given by stochastic dependency context free grammar (SDCFG). Japanese broadcast news speech transcribed using a large-vocabulary continuous-speech recognition (LVCSR) system is summarized using our proposed method and compared with manual summarization by human subjects. The manual summarization results are combined to build a word network. This word network is used to calculate the word accuracy of each automatic summarization result using the most similar word string in the network. Experimental results show that the proposed method effectively extracts relatively important information by removing redundant and irrelevant information.

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