@InProceedings{tatman:2017:CMCL,
  author    = {Tatman, Rachael},
  title     = {``Oh, I've Heard That Before": Modelling Own-Dialect Bias After Perceptual Learning by Weighting Training Data},
  booktitle = {Proceedings of the 7th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2017)},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {29--34},
  abstract  = {Human listeners are able to quickly and robustly adapt to new accents and do so
	by using information about speaker's identities. This paper will present
	experimental evidence that, even considering information about speaker's
	identities, listeners retain a strong bias towards the acoustics of their own
	dialect after dialect learning. Participants' behaviour was accurately mimicked
	by a classifier which was trained on more cases from the base dialect and fewer
	from the target dialect. This suggests that imbalanced training data may result
	in automatic speech recognition errors consistent with those of speakers from
	populations over-represented in the training data.},
  url       = {http://www.aclweb.org/anthology/W17-0704}
}