Abstract
A segmental probabilistic model based on an orthogonal polynomial representation of speech signals is proposed. Unlike the conventional frame based probabilistic model, this segment based model concatenates the similar acoustic characteristics of consecutive frames into an acoustic segment and represents the segment by an orthogonal polynomial function. An iterative algorithm that performs recognition and segmentation processes is proposed for estimating the segment model. This segment model is applied in the text independent speaker verification. Tests were carried out on a 20-speaker database. With the best version of the model, an equal error rate of 0.59% can be reached, for test utterances of 10 digits. This corresponds to a relative error rate reduction of more than 50%, compared to the conventional frame based probabilistic model.
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