Algorithmic Classification of Paraphasias

项目摘要 此应用程序的母基金R01DC015999专注于开发自动化 用于识别和分类语言样本中的副相语音错误的系统 中风后失语症患者，无论是在对抗命名测试的背景下， 在连接的讲话。目前的方法需要手动地 转录，这是既耗时又容易出错，并限制了临床适用性 技术。自父母补助金制定以来， 自动语音识别（ASR）技术，并且可能很快就可以实现自动化 转录步骤。这将为应用自动化系统开辟许多新的途径。 排序下开发的父母补助金，无论是在临床和研究环境。但这些 有前途的新ASR技术依赖于大型和仔细注释的数据集， 目前还不存在的失语症语言。根据这一行政补充规定，我们 我建议通过开展广泛的转录运动和详细的 注释已经存在的公开可用的失语症录音库 言语，包括结构化命名测试和话语样本。除了音素 转录的话语本身，我们将注释其他功能的失语症讲话（假 开始、不流利等）以支持自动算法的开发， 分析这样的言论。我们的跨学科机器学习研究团队和 失语症学家将密切合作，制作一个所需的精选数据集， 开发、训练和评估用于语音识别的现代机器学习技术。 重要的是，将以与其他类似的方式记录和组织所产生的数据集。 大规模的ASR数据集，并将公开发布给临床和机器学习 社区.为了提高对数据集（以及一般问题空间）的认识， 在机器学习社区内，我们进一步建议组织一次共享评估 任务，参与团队将利用我们的最终数据集来构建自动化的 命名测试的转录系统，这将在“烘烤”设置进行比较。

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.