Breaking the Barriers of Text-Hungry and Audio-Deficient AI
Abstract
While global linguistic diversity spans more than 7164 recognized languages, the current dominant architecture of machine intelligence remains fundamentally biased toward written text. This bias excludes over 700 million people particularly in rural and remote regions who are audio-literate. In this work, we introduce a fully textless, audio-to-audio machine intelligence framework designed to serve this underserved population, and all the people who prefer audio-efficiency. Our contributions include novel Audio-to-Audio translation architectures that bypass text entirely, including spectrogram-, scalogram-, wavelet-, and unit-based models. Central to our approach is the Multiscale Audio-Semantic Transform (MAST), a representation that encodes tonal, prosodic, speaker, and expressive features. We further integrate MAST into a fractional diffusion of mean-field-type framework powered by fractional Brownian motion. It enables the generation of high-fidelity, semantically consistent speech without reliance on textual supervision. The result is a robust and scalable system capable of learning directly from raw audio, even in languages that are unwritten or rarely digitized. This work represents a fundamental shift toward audio-native machine intelligence systems, expanding access to language technologies for communities historically left out of the current machine intelligence ecosystem.
Keywords
Cite
@article{arxiv.2506.02443,
title = {Breaking the Barriers of Text-Hungry and Audio-Deficient AI},
author = {Hamidou Tembine and Issa Bamia and Massa NDong and Bakary Coulibaly and Oumar Issiaka Traore and Moussa Traore and Moussa Sanogo and Mamadou Eric Sangare and Salif Kante and Daryl Noupa Yongueng and Hafiz Tiomoko Ali and Malik Tiomoko and Frejus Laleye and Boualem Djehiche and Wesmanegda Elisee Dipama and Idris Baba Saje and Hammid Mohammed Ibrahim and Moumini Sanogo and Marie Coursel Nininahazwe and Abdul-Latif Siita and Haine Mhlongo and Teddy Nelvy Dieu Merci Kouka and Mariam Serine Jeridi and Mutiyamuogo Parfait Mupenge and Lekoueiry Dehah and Abdoul Aziz Bio Sidi Bouko and Wilfried Franceslas Zokoue and Odette Richette Sambila and Alina RS Mbango and Mady Diagouraga and Oumarou Moussa Sanoussi and Gizachew Dessalegn and Mohamed Lamine Samoura and Bintou Laetitia Audrey Coulibaly},
journal= {arXiv preprint arXiv:2506.02443},
year = {2025}
}
Comments
61 pages, 16 figures, 14 tables, 25 languages, 13 blockaudio per language. Presented at AI Mali, May 2025