Abstract: A method of creating an application-generic class-based SLM includes, for each of a plurality of speech applications, parsing a corpus of utterance transcriptions to produce a first output set, in which expressions identified in the corpus are replaced with corresponding grammar tags from a grammar that is specific to the application. The method further includes, for each of the plurality of speech applications, replacing each of the grammar tags in the first output set with a class identifier of an application-generic class, to produce a second output set. The method further includes processing the resulting second output sets with a statistical language model (SLM) trainer to generate an application-generic class-based SLM.

Abstract: A method and apparatus for use in the field of speaker verification. The invention provides a method and apparatus for generating a pair of data elements, namely a first element representative of a speaker specific speech pattern and a second element representative of a biased normalizing template, the pair of data elements being suitable for use in a speaker verification system. The invention provides receiving an audio signal representative of a training token associated with a given speaker and processing the training token on a basis of a reference speaker independent model set to derive a speaker independent normalizing template. The invention further provides processing the training token on a basis of a reference speaker independent model set for generating a speaker specific speech pattern. The speaker specific speech pattern and the speaker independent normalizing template are then processed to derive a biased normalizing template.