Anomia (word-finding difficulties) is the hallmark of aphasia, an acquired language disorder most commonly caused by stroke. Assessment of speech performance using picture naming tasks is a key method for both diagnosis and monitoring of responses to treatment interventions by people with aphasia (PWA). Currently, this assessment is conducted manually by speech and language therapists (SLT). Surprisingly, despite advancements in automatic speech recognition (ASR) and artificial intelligence with technologies like deep learning, research on developing automated systems for this task has been scarce. Here we present NUVA, an utterance verification system incorporating a deep learning element that classifies 'correct' versus' incorrect' naming attempts from aphasic stroke patients. When tested on eight native British-English speaking PWA the system's performance accuracy ranged between 83.6% to 93.6%, with a 10-fold cross-validation mean of 89.5%. This performance was not only significantly better than a baseline created for this study using one of the leading commercially available ASRs (Google speech-to-text service) but also comparable in some instances with two independent SLT ratings for the same dataset.

失语症（找词困难）是失语症的标志，失语症是一种最常由中风引起的后天语言障碍。使用图片命名任务评估语音性能是诊断和监测失语症 (PWA) 患者对治疗干预反应的关键方法。目前，这项评估是由言语和语言治疗师 (SLT) 手动进行的。令人惊讶的是，尽管使用深度学习等技术在自动语音识别 (ASR) 和人工智能方面取得了进步，但针对此任务开发自动化系统的研究却很少。在这里，我们介绍了 NUVA，这是一种话语验证系统，它结合了深度学习元素，可以对失语症中风患者的“正确”命名尝试与“不正确”命名尝试进行分类。在 8 个以英式英语为母语的 PWA 上进行测试时，系统的性能准确度在 83.6% 到 93.6% 之间，10 倍交叉验证平均值为 89.5%。这种性能不仅显着优于使用领先的商用 ASR（谷歌语音到文本服务）之一为本研究创建的基线，而且在某些情况下与同一数据集的两个独立 SLT 评级相当。