Bayesian methods for language verification

This paper describes a number of techniques for language verification based on acoustic processing and n-gram language modelling. A new technique is described which uses anti-models to model the general class of languages. These models are then used to normalise the acoustic score giving a 34% reduction in the error rate of the system. An approach to automatically generate discriminative subword strings for language verification is presented. The occurrence of recurrent strings are scored using a Poisson-based significance test. It is shown that when significant substrings do occur in the test material they are strong indicators of the target language occurring.