This paper presents an experimental study of a maximum likelihood (ML) approach to irrelevant variability normalization (IVN) based training and unsupervised online adaptation for large vocabulary continuous speech recognition. A moving-window based frame labeling method is used for acoustic sniffing. The IVN-based approach achieves a 10% relative word error rate reduction over an ML-trained baseline system on a Switchboard-1 conversational telephone speech transcription task.
Bibliographic reference. Shi, Guangchuan / Shi, Yu / Huo, Qiang (2010): "A study of irrelevant variability normalization based training and unsupervised online adaptation for LVCSR", In INTERSPEECH-2010, 1357-1360.