We present a language-specific word-level noise injection method for low-resource machine translation that eliminates the need for an external monolingual corpus by constructing the low-resource language vocabulary directly from the source-side text of the target dataset. We also investigate two important hyperparameters of the original method: the maximum edit distance used for candidate word selection and the target noise ratio. Specifically, we propose a dynamic edit-distance constraint based on word length and evaluate multiple noise ratios from 5% to 30%. Experiments on Assamese-English translation show that the proposed dynamic constraint consistently outperforms the fixed edit-distance setting across all evaluated noise ratios, while a moderate noise ratio achieves the best translation performance. We further report our official results for the WMT 2026 Low-Resource Indic Language Translation Shared Task and discuss the effectiveness and limitations of the proposed approach under different resource conditions.