Language identification using shifted delta cepstra

A variety of speech identification technologies currently use Gaussian mixture models. Until recently, however, they were considered inferior to parallel phone recognition language modeling for identifying the language of a speaker. Experiments in the last year have shown that Gaussian mixture models can provide high performance language identification when shifted delta cepstra are used as the feature set. Not only can they achieve comparative or even superior performance to parallel phone recognition language modeling, Gaussian mixture models also require less computation. Performance can be further improved by altering the shifted delta cepstra parameters and the number of mixtures. The optimal parameter set varies depending on the languages to be identified. This paper describes a method for finding the optimal parameters for identifying a set of languages, specifies these parameters for a language identification task, and provides a performance comparison.