Current state-of-the-art speech models often pass low-resource languages through French or English text encodings before acoustic mapping. This results in massive linguistic degradation, as phonetic nuances of languages like Baoulé, Dyula, or Wolof are completely lost in translation.
Our research lab at DipR AI rejects this textual filter. We are training audio-native tokenizers directly on regional acoustic data. By bypasssing traditional text representation, we capture authentic tonality, local accents, and oral-only dialects.
Our preliminary tests show a 35% reduction in word error rate (WER) across Central and West African languages, establishing a new frontier for frugal language modeling.