Khairun-nisa Hassanali 1 , Yang Liu 1 and Thamar Solorio 2

1. 6. Results 7. Experimental Results 7. Conclusion 3. Data 4. Features 5. Experiment Setup 1. Summary 2. The Larger Problem Evaluating NLP Features for Automatic Prediction of Language Impairment Using Child Speech Transcripts INTERSPEECH 2012 • Deeper NLP features are explored for automatic prediction of Language Impairment (LI) • Narrative structure and narrative quality features are also explored for the automatic prediction of LI for story telling tasks • Narrative structure and quality features along with a combination of other features are helpful in the prediction of LI on story telling narratives Khairun-nisa Hassanali1, Yang Liu1 and Thamar Solorio2 nisa@hlt.utdallas.eduyangl@hlt.utdallas.edusolorio@uab.edu 1The University of Texas at Dallas 2University of Alabama at Birmingham • Detecting language impairment (LI) in children • Traditional methods of detecting LI • Cutoff methods on norm referenced tests • Time consuming • May result in over and under identification of LI • Automatic detection of LI is faster and allows for exploring more features beyond norm referenced tests • Given a child language transcript, answer the following question: • Does the transcript belong to a typically developing (TD) child or a child with LI? • What features are useful in detecting LI? • Bayesian network classifier performed the best • Performed feature selection • Instantiation of story, number of cognitive references and number of social engagement devices were top scoring NSQ features • Transcripts of adolescents aged 14 years, for two tasks: • Story telling task • Spontaneous personal narrative • 118 speakers (99 TD children, 18 LI children) • 118 transcripts for each task • Annotated story telling transcripts for coherence and narrative structure features • Treat prediction of LI as a binary classification task • Used features in prior work by Gabani et al. as a baseline • Feature categories 2- 6: from Coh-Metrix tool • Entity grid model features: Brown coherence toolkit • Narrative structure and quality features: based on manual annotation for the story telling narratives • Used leave-one-out cross validation • Classification experiments: WEKA • Evaluate various features for automatic prediction of LI from child language transcripts • General word and text, and syntactic features perform better for spontaneous narratives • Referential and semantic and entity grid features perform better for story telling narratives • Narrative structure quality features led to an increase of 8.7% over baseline for spontaneous narratives