Incremental predictive and adaptive noise compensation

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 CMLLR. These predictive schemes can also be combined 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.