An orthogonal polynomial representation of speech signals and its probabilistic model for text independent speaker verification

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 algorithm which iteratively performs recognition and segmentation processes is proposed for estimating the parameters of the segment model. This segment model is applied in the text independent speaker verification. For a 20-speaker database, the experimental results show that the performance by using segment models is better than that by using the conventional frame based probabilistic model. The equal error rate can be reduced by 3.6% when the models are represented by 64-mixture density functions.