The authors consider artificial intelligence possibilities in the functional diagnosis of phonological and phonetic decoding of bilingual speech, using the example of one of the indigenous peoples of the North Caucasus—the Avars. The relevance of the research is due to its interdisciplinarity and use of different methods and approaches to analyzing bilingual speech. The corpus of audio files of assimilative interaction of minority languages of the North Caucasus is processed in Praat 5.3.32. The acoustic and prosodic parameters are marked, normalized, and tokenized. The sampling procedure is performed using the signal processing libraries Librosa, Parselmouth, and a script developed by Goncharova for acoustic and prosodic analysis in Python 3.11. During the prosodic and acoustic analyses, the authors proved the predominance of large pauses in the target bilingual performer’s speech. Moreover, the authors identified certain deviations in the pronunciation norm for certain consonants and minor changes in the frontness (or backness) and height of the vowels /и/ and /a/, which appeared due to the interference of the qualitative characteristics of the Avar vowels. The scientific novelty of this research is determined by the description of the programming stage algorithm and the flexibility of the proposed model. The presented approach can be extended to other languages.