We propose a technique to port channel characteristics from one language to another. This allows us to build acoustic models in a target language that are robust to an environment for which we have no data in that language.

The approach consists in training broad phonetic class maximum likelihood linear regression (MLLR) transformations from a source language, and applying them in the target language. These transforms encapsulate the acoustic specificities of the environment without capturing language-specific characteristics that are difficult to port across languages.

As a case study, we consider the problem of building in-the-car GSM models for UK English, assuming that we have no GSM, and no car data in UK English, but that we have such data in German. We show that this technique can greatly reduce the error rate of the recognition system on English GSM car data.