Language barriers limit access to communication across fields in healthcare, media, public speaking, and other spaces. Bridging the gap in language comprehension often requires manual effort, such as subtitle translation for movies or professional interpreters in a doctor’s office. While there are AI recognition tools that offer some assistance in current translation procedures, such protocols are limited by their performance and inability to translate as well as an experienced human speaker. Recent advances in large language models, however, present an opportunity to iterate and improve AI performance throughout this process. In prior work, I analyzed how multi-agent communication can influence interpretation accuracy within medical systems. I seek to expand this work in the domain of mass media, creating a protocol to interpret audio with multi-lingual dialects into a transcript of any language choice. The methodology involves extracting dialogue from previous radio transcripts, generating raw multilingual transcripts using speech recognition tools, segmenting changes in language, and translating non-English spoken parts to form one full transcript in English. Statistical analysis using the metrics METEOR and BLEU will provide evidence of protocol translationability. Future applications could include expanding datasets across local radio stations for general implementation.