Algorithmic Classification of Paraphasias

项目总结

Project Summary This application’s parent grant, R01DC015999, is focused on the development of automated systems for identifying and categorizing paraphasic speech errors in language samples from individuals with post-stroke aphasia, both in the context of confrontation naming tests as well as in connected speech. Current approaches require that language samples be manually transcribed, which is both time-consuming and error-prone, and limits the clinical applicability of the technology. Since the parent grant was written, there have been major improvements in automatic speech recognition (ASR) technology, and it may soon be possible to automate this transcription step. This would open many new avenues for applying automated systems of the sort developed under the parent grant, both in clinical and research settings. However, these promising new ASR techniques depend on large and carefully-annotated datasets, of the sort that do not exist currently for aphasic speech. Under this administrative supplement, we propose to address this issue by performing an extensive campaign of transcription and detailed annotation of an already-existing publicly-available library of audio recordings of aphasic speech, including both structured naming tests and discourse samples. In addition to phonemic transcription of utterances themselves, we will annotate other features of aphasic speech (false starts, disfluencies, etc.) so as to support the development of automated algorithms for analyzing such speech. Our interdisciplinary team of machine learning researchers and aphasiologists will collaborate closely to produce a curated dataset of the sort needed to develop, train, and evaluate modern machine learning techniques for speech recognition. Importantly, the resulting dataset will be documented and organized in a similar manner to other large-scale ASR datasets, and will be released publicly to both the clinical and machine learning communities. In order to raise awareness of the dataset (and of this problem space in general) within the machine learning community, we further propose to organize a shared evaluation task, in which participating teams will make use of our final dataset to build automated transcription systems for naming tests, which will be compared in a “bakeoff” setting.