Paper: Discriminative Joint Modeling of Lexical Variation and Acoustic Confusion for Automated Narrative Retelling Assessment

ACL ID N13-1021
Title Discriminative Joint Modeling of Lexical Variation and Acoustic Confusion for Automated Narrative Retelling Assessment
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

Automatically assessing the fidelity of a retelling to the original narrative ? a task of growing clinical importance ? is challenging, given extensive paraphrasing during retelling along with cascading automatic speech recog- nition (ASR) errors. We present a word tag- ging approach using conditional random fields (CRFs) that allows a diversity of features to be considered during inference, including some capturing acoustic confusions encoded in word confusion networks. We evaluate the approach under several scenarios, including both supervised and unsupervised training, the latter achieved by training on the output of a baseline automatic word-alignment model. We also adapt the ASR models to the domain, and evaluate the impact of error rate on per- formance. We find strong robustness to ...