We introduce UTER, a novel automatic translation evaluation metric specifically designed for morphologically complex languages. Unlike traditional TER approaches, UTER incorporates a reordering algorithm and leverages the Sørensen-Dicse similarity measure to better account for morphological variations.Tested on morphologically rich and low resource languages from the WMT22 dataset, such as Finnish, Estonian, Kazakh, and Xhosa, UTER delivers results that align more closely with human direct assessments (DA) and outperforms benchmark metrics, including chrF and METEOR. Furthermore, its effectiveness has also been demonstrated on languages with complex writing systems, such as Chinese and Japanese, showcasing its versatility and robustness.

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.