Word Error Rate (WER) is a crucial metric for evaluating the performance of automatic speech recognition (ASR) systems. However, its traditional calculation, based on Levenshtein distance, does not account for lexical similarity between words and treats each substitution in a binary manner, while also ignoring segmentation errors. This paper proposes an improvement to WER by introducing a weighted substitution method, based on lexical similarity measures, and incorporating splitting and merging operations to better handle segmentation errors. Unlike other WER variants, our approach is easily integrable and generalizable to various languages, providing a more nuanced and accurate evaluation of ASR transcriptions, particularly for morphologically complex or low-resource languages.