Automatic speech recognition in the diagnosis of primary progressive aphasia

Narrative speech can provide a valuable source of information about an individual’s linguistic abilities across lexical, syntactic, and pragmatic levels. However, analysis of narrative speech is typically done by hand, and is therefore extremely time-consuming. Use of automatic speech recognition (ASR) software could make this type of analysis more efficient and widely available. In this paper, we present the results of an initial attempt to use ASR technology to generate transcripts of spoken narratives from participants with semantic dementia (SD), progressive nonfluent aphasia (PNFA), and healthy controls. We extract text features from the transcripts and use these features, alone and in combination with acoustic features from the speech signals, to classify transcripts as patient versus control, and SD versus PNFA. Additionally, we generate artificially noisy transcripts by applying insertions, substitutions, and deletions to manually-transcribed data, allowing experiments to be conducted across a wider range of noise levels than are produced by a tuned ASR system. We find that reasonably good classification accuracies can be achieved by selecting appropriate features from the noisy transcripts. We also find that the choice of using ASR data or manually transcribed data as the training set can have a strong effect on the accuracy of the classifiers.