Speaker Ensembles Recognition In Different Noisy Environments
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Abstract
The recognition process comprises two phases, the offline and the online. In the offline phase, we prepare an ensemble speaker and speaking environment space formed by a collection of super-vectors.Each super-vector consists of the entire set of means from all the Gaussian mixture components of a set of Hidden Markov models that characterizes a particular environment. In the online phase, with the ensemble environment space prepared in the offline phase, we estimate the super-vector for a new testing environment based on a stochastic matching criterion. In this paper, we focus on methods for enhancing the construction and coverage of the environment space in the offline phase.We first demonstrate environment clustering and partitioning algorithms to structure the environment space well; then, we propose a minimum classification error training algorithm to enhance discrimination across environment super-vectors and therefore broaden the coverage of the ensemble environment space.
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Index Terms—Environment modeling, noise robustness.
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