Model compensation schemes are a powerful approach to handling mismatches between training and testing conditions. Normally these schemes are run in a batch adaptation mode, re-recognising the utterance used to estimate the noise model parameters. For many applications this introduces unacceptable latency. This paper examines three forms of incremental mode model-based compensation: vector Taylor series; joint uncertainty decoding; and predictive CM-LLR. These predictive schemes can also be

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... ined with adaptive schemes such as CMLLR. By combining the approaches, weaknesses of each can be addressed. The performance is evaluated on in-car recorded data, where the combined incremental scheme shows gains over either individually.