INTERSPEECH 2010
11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

A Study of Irrelevant Variability Normalization Based Training and Unsupervised Online Adaptation for LVCSR

Guangchuan Shi, Yu Shi, Qiang Huo

Microsoft Research, China

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.

Full Paper

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.