This study reports on an ongoing systematic review of empirical research on automatic corrective feedback for second language (L2) speaking in CALL contexts. As AI-mediated and ASR-based feedback tools are increasingly integrated into speaking practice, research findings remain dispersed across feedback designs, outcome measures, and instructional settings. In addition, due to the rapidly evolving nature of language learning technologies, there is a continual need for up-to-date research syntheses. Following PRISMA-informed procedures, an initial search was conducted to identify studies examining automated feedback systems targeting L2 speaking. Preliminary screening has identified a subset of studies addressing speaking-related outcomes, with most research focusing on overall speaking proficiency, grammar, and pronunciation. In contrast, fluency-related outcomes appear to be comparatively underexplored. Studies are being coded for feedback modality, targeted speaking construct, learning context, and outcome domain, including both linguistic development and learner-related variables such as motivation, confidence, and willingness to communicate. By synthesizing methodological patterns and reported outcomes, the study aims to identify gaps and design challenges in current CALL research on automatic speaking feedback. The findings will inform CALL researchers and practitioners about how automatic feedback is currently operationalized and highlight directions for future research on technology-mediated support for L2 speaking.