Accent variability is an important factor in speech that can significantly degrade automatic speech recognition performance. We investigate the effect of multiple accents on an English broadcast news recognition system. A multi-accented English corpus is used for the task, including broadcast news segments from 6 different geographic regions: US, Great Britain, Australia, North Africa, Middle East and India. There is significant performance degradation of a baseline system trained on only US data when confronted with shows from other regions. The results improve significantly when data from all the regions are included for accent-independent acoustic model training. Further improvements are achieved when MAP-adapted accent-dependent models are used in conjunction with a GMM accent classifier.
Bibliographic reference. Vergyri, Dimitra / Lamel, Lori / Gauvain, Jean-Luc (2010): "Automatic speech recognition of multiple accented English data", In INTERSPEECH-2010, 1652-1655.