The Word Error Rate metric is a fundamental measurement for assessing the accuracy of automatic speech recognition (ASR) and machine translation systems. It quantifies how closely a system's output matches a reference transcript by measuring discrepancies between them. The metric is calculated using dynamic programming algorithms like Levenshtein distance, which identifies the minimal number of edits needed to transform the system's output into the correct transcript. Understanding the Word Error Rate metric is crucial for improving language processing technologies and ensuring accurate results in applications such as speech recognition, machine translation, healthcare, automotive voice control systems, accessibility, and cultural heritage preservation projects. The metric is used to evaluate AI models' performance and is often set as a threshold that must be met before new models can be deployed to production.