This research describes methods for annotating Russian spoken speech using machine processing of linguistic materials. For the first time, the concept of “annotation” is defined as a method of data processing and interpretation based on a given code involving information compression through special linguistic paraphrasing algorithms. The authors analyzed traditional approaches to phonemic and orthographic markup, along with their limitations in conveying allophonic variability inherent in the Russian language. Special attention is paid to the challenges of automatic annotation, including the choice of labels (phonemic, acoustic, or orthographic), which complicates corpus compatibility. The authors discussed advanced annotation tools (e.g., the Montreal Forced Aligner) and considered their limitations in modeling key phonological processes of Russian, including vowel reduction and consonant assimilation. The necessity of metadata standardization and adaptive ontologies that account for diachronic differences in the approaches of the Moscow and St. Petersburg phonological schools is emphasized. Moreover, the authors examined promising methods for overcoming these challenges, including multimodal architectures for processing dialectal variations, the integration of semantic models (BERT) to resolve homophony, and the use of deep neural networks (Conformer) for context-dependent speech analysis. The research concludes on the importance of creating benchmark corpora with expert annotation and designing end-to-end architectures integrating spectral feature extraction with deep semantic contextualization to improve the quality of automatic phonetic annotation.