Using Hidden Markov Models in segmentation of speaker-independent connected-digits corpus
نویسنده
چکیده
The first task to be accomplished in speech recognition is the segmentation and labeling of records. Regarding speech, this is a very complicated and costly procedure, although of most importance because at the present time many available speech corpora are not segmented. This paper proposes a semi-automatic segmentation method in order to reduce the manual segmentation burden of a very large corpus. First, Hidden Markov Models are created with a reduced set of records. Afterwards they are used to perform an automatic segmentation on the rest. Recursively, new more robust models are created and used to create new segmentations. The method consists in three main steps: (1) Initial Reduced Segmentation, (2) RecursiveExtended Segmentation and (3) Post-processing of the labels. This method was evaluated in the segmentation of the TIDIGITS corpus with two independent initial manual segmentations. Finally the method was able to label correctly 96.18 % and 95.72 % of the corpus records, respectively. Key-Words: Automatic annotation, Speech segmentation, Markov Models, Speech recognition
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تاریخ انتشار 2002