An Unsupervised Model of Infant Acoustic Speech Segmentation
نویسنده
چکیده
There is a long standing hypothesis in Developmental Psychology that children use statistical information to segment acoustic speech streams into words. Additionally, several experiments have demonstrated that infants are able to find word breaks using distributional cues. In this paper we propose an algorithm for the unsupervised segmentation of audio speech, based on the Voting Experts (VE) algorithm. We show that this algorithm can reproduce results obtained from segmentation experiments performed with 8-month-old infants.
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تاریخ انتشار 2009