Large-scale spontaneous speech corpora are crucial resource for various domains of spoken language processing. However, the available corpora are usually limited because their construction cost is quite expensive especially in transcribing speech precisely. On the other hand, loosely transcribed corpora like shorthand notes, meeting records and closed captions are more widely available than precisely transcribed ones, because their imperfectness reduces their construction cost. Because these corpora contain both precisely transcribed regions and edited regions, it is difficult to use them directly as speech corpora for learning acoustic models. Under this background, we have been considering to build an efficient semi-automatic framework to convert loose transcriptions to precise ones. This paper describes an improved automatic detection method of precise regions from loosely transcribed corpora for the above framework. Our detection method consists of two steps: the first step is a force alignment between loose transcriptions and their utterances to discover the corresponding utterance for the certain loose transcription, and the second step is a detector of precise regions with a support vector machine using several features obtained from the first step. Our experimental result shows that our method achieves a high accuracy of detecting precise regions, and shows that the precise regions extracted by our method are effective as training labels of lightly supervised speaker adaptation.

This paper explains our developing Corpus of Japanese classroom Lecture speech Contents (henceforth, denoted as CJLC). Increasing e-Learning contents demand a sophisticated interactive browsing system for themselves, however, existing tools do not satisfy such a requirement. Many researches including large vocabulary continuous speech recognition and extraction of important sentences against lecture contents are necessary in order to realize the above system. CJLC is designed as their fundamental basis, and consists of speech, transcriptions, and slides that were collected in real university classroom lectures. This paper also explains the difference about disfluency acts between classroom lectures and academic presentations.