@inproceedings{Fraseretal2015,
   author = {Kathleen C. Fraser and Naama Ben-David and Graeme Hirst
                  and Naida L. Graham and Elizabeth Rochon},
   title = {Sentence segmentation of aphasic speech},
   address = {Denver},
   booktitle = {2015 Conference of the North American Chapter of the
                  Association for Computational Linguistics -- Human
                  Language Technologies (NAACL-HLT-2015)},
   pages = {862--871},
   year = {2015},
   month = {June},
   download = {http://ftp.cs.toronto.edu/pub/gh/Fraser-etal-2015.pdf},
   abstract = {Automatic analysis of impaired speech for screening or
                  diagnosis is a growing research field; however there
                  are still many barriers to a fully automated
                  approach. When automatic speech recognition is used
                  to obtain the speech transcripts, sentence
                  boundaries must be inserted before most measures of
                  syntactic complexity can be computed. In this paper,
                  we consider how language impairments can affect
                  segmentation methods, and compare the results of
                  computing syntactic complexity metrics on
                  automatically and manually segmented transcripts. We
                  find that the important boundary indicators and the
                  resulting segmentation accuracy can vary depending
                  on the type of impairment observed, but that results
                  on patient data are generally similar to control
                  data. We also find that a number of syntactic
                  complexity metrics are robust to the types of
                  segmentation errors that are typically made.}
}

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