Dialectal Adaptation of Foundation Models for Low-Resource Speech Synthesis: A Case Study on Adamawa Fulfulde

The rapid evolution of neural speech synthesis has achieved near-human naturalness for high-resource languages, yet a profound digital divide persists for the linguistically diverse yet digitally underserved regions of Africa. Fulfulde, a major West and Central African macro language, exemplifies this disparity. While recent foundation models like Massively Multilingual Speech (MMS) provide zero-shot synthesis capabilities for Fulfulde, they often fail to capture the nuanced phonetic signatures of specific dialects, such as the Adamawa variety spoken in Cameroon. This study presents a robust framework for the dialectal adaptation of foundation models under extreme data scarcity. Rather than claiming a "first-of-its-kind" system, we demonstrate that targeted fine-tuning of a VITS-based architecture significantly outperforms the generalized zero-shot outputs of state-of-the-art multilingual models. Utilizing a meticulously curated 100-minute corpus and a specialized orthographic normalization protocol, our system achieves a Mean Opinion Score (MOS) of 4.25, as evaluated by a panel of 24 native speakers. The results indicate that dialect-specific fine-tuning is essential for accurately modeling glottalic airstream mechanisms (implosives) and vowel quantity contrasts, which are frequently marginalized in generalized multilingual frameworks. This work provides a rigorous methodology for localized linguistic inclusion in the era of foundation models.