Aphasia is an acquired language disorder resulting from brain damage that can cause significant communication difficulties. Aphasic speech is often characterized by errors known as paraphasias, the analysis of which can be used to determine an appropriate course of treatment and to track an individual’s recovery progress. Being able to detect paraphasias automatically has many potential clinical benefits; however, this problem has not previously been investigated in the literature. In this paper, we perform the first study on detecting phonemic and neologistic paraphasias from scripted speech samples in AphasiaBank. We propose a speech recognition system with task-specific language models to transcribe aphasic speech automatically. We investigate features based on speech duration, Goodness of Pronunciation, phone edit distance, and Dynamic Time Warping on phoneme posteriorgrams. Our results demonstrate the feasibility of automatic paraphasia detection and outline the path toward enabling this system in real-world clinical applications.