Synthetic Language Interpretation Modality, Machine Learning

The Synthetic Language Interpretation Modality, Machine Learning (SLIM-ML) is a military use-restricted algorithm devised by the UNSC Office of Naval Intelligence for the computational interpretation and recognition of virtually all forms of human speech, encompassing most 26th century major languages and their variegated dialects. Built by progressive machine learning artificial intelligence-training approaches, the SLIM-ML algorithm was "trained" on 1030 instances of human speech collected from a staggeringly diverse compendium of recorded pieces of human speech spanning centuries of terran history. Founded on this vast "training set" of human speech fragments, SLIM-ML can confidently understand and digitize the speech of virtually any human being alive, regardless of the tone of the speaker's voice, their base language, any regional dialects, or digital distortion (e.g., voices over secure channels that are scrambled for anonymity purposes). SLIM-ML is presently being trained on a repertoire of Covenant languages and dialects, based on massive miscellaneous commercial, historical, and governmental speech fragments provided by the new Sangheili-led Covenant regime.