Kernel Eigenvoices (Revisited) for Large-Vocabulary Speech Recognition

Kernelized eigenvoice methods, which apply a nonlinear transform in speaker space, have previously been proposed for rapid adaptation. This paper examines, and addresses, a number of limitations and issues with the current schemes. First, the requirements for valid probability functions using kernel representations are discussed. Second, rapid speaker adaptation using these forms of representations is analyzed and the general update formulae for kernelized eigenvoice adaptation derived. The existing kernelized eigenvoice methods are then described within this formulation. This allows an EM-based, rather than gradient-descent-based, parameter estimation. To enable these approaches to be applied to large-vocabulary speech recognition tasks, eigenbases using transformations of an underlying canonical model are described and related to existing adaptation methods. Preliminary experiments on a large-vocabulary conversational telephone speech task are finally detailed.