|Title:||Eigenvalues Driven Gaussian Selection in continuous speech recognition using HMMs with full covariance matrices||Journal:||Applied Intelligence||Volume:||33||Issue:||2||First page:||107||Last page:||116||Issue Date:||1-Oct-2010||Rank:||M23||ISSN:||0924-669X||DOI:||10.1007/s10489-008-0152-9||Abstract:||
In this paper a novel algorithm for Gaussian Selection (GS) of mixtures used in a continuous speech recognition system is presented. The system is based on hidden Markov models (HMM), using Gaussian mixtures with full covariance matrices as output distributions. The purpose of Gaussian selection is to increase the speed of a speech recognition system, without degrading the recognition accuracy. The basic idea is to form hyper-mixtures by clustering close mixtures into a single group by means of Vector Quantization (VQ) and assigning it unique Gaussian parameters for estimation. In the decoding process only those hyper-mixtures which are above a designated threshold are selected, and only mixtures belonging to them are evaluated, improving computational efficiency. There is no problem with the clustering and evaluation if overlaps between the mixtures are small, and their variances are of the same range. However, in real case, there are numerous models which do not fit this profile. A Gaussian selection scheme proposed in this paper addresses this problem. For that purpose, beside the clustering algorithm, it also incorporates an algorithm for mixture grouping. The particular mixture is assigned to a group from the predefined set of groups, based on a value aggregated from eigenvalues of the covariance matrix of that mixture using Ordered Weighted Averaging operators (OWA). After the grouping of mixtures is carried out, Gaussian mixture clustering is performed on each group separately.
|Keywords:||Constrained optimization | Eigenvalues | Gaussian mixtures | Gaussian selection | Ordered weighted averaging operators | Vector quantization||Publisher:||Springer Link|
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