Algorithmic Classification of Paraphasias

项目摘要 此应用程序的父授权R01DC015999专注于自动化的开发 用于识别和分类来自 卒中后失语的个体，在对峙命名测试以及 在连通的演讲中。目前的方法需要手动提供语言样本 转录，这既耗时又容易出错，并限制了临床应用 这项技术。自家长拨款写成以来，在以下方面有了重大改进 自动语音识别(ASR)技术，可能很快就会实现自动化 转录步骤。这将为应用自动化系统开辟许多新的途径 在父母资助下开发的类型，在临床和研究环境中都有。然而，这些 前景看好的新ASR技术依赖于这类经过仔细注释的大型数据集 目前还不存在用于失语症语音的。根据这项行政补充资料，我们 建议通过开展广泛的转录活动来解决这个问题，并详细说明 已有的公开提供的失语症录音资料库的注释 语音，包括结构化命名测试和语篇样本。除了音素之外 话语本身的转录，我们将注释失语症言语的其他特征(假 开始、不流畅等)以支持自动算法的开发 分析这样的演讲。我们的机器学习研究人员和跨学科团队 失语症专家将密切合作，以产生所需的经过精心挑选的数据集 开发、培训和评估用于语音识别的现代机器学习技术。 重要的是，生成的数据集将以与其他数据集相似的方式进行记录和组织 大规模的ASR数据集，并将公开发布给临床和机器学习 社区。为了提高人们对数据集(以及一般的问题空间)的认识 在机器学习社区内，我们还建议组织一次共享评估 任务，在该任务中，参与团队将利用我们的最终数据集来构建自动化 命名测试的转录系统，将在“烘焙”设置中进行比较。

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.