Bilingual speech separation presents significant challenges and opportunities in the field of speech processing. Vietnamese and English differ fundamentally in their phonetic structures. Vietnamese is a tonal language, while English relies on stress patterns and non-tonal distinctions. This disparity complicates the separation of mixed-language speech, as traditional models may struggle to accurately distinguish overlapping phonemes and words from the two languages. The MossFormer2 is an advanced model combining Transformer-based self-attention mechanisms with a feedforward sequential memory network, enhances the ability to model both long-range dependencies and fine-scale recurrent patterns in speech signals. This hybrid approach improves the separation of mixed-language audio by effectively handling the unique acoustic characteristics of both Vietnamese and English. By leveraging this model, researchers achieve more accurate separation of bilingual speech mixtures, facilitating clearer transcription and analysis in multilingual environments.