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What LID Systems Say About Dialectal Variation. The Case of Yiddish, Quechua and Mande

This study investigates the ability of speech-based language identification (LID) systems to handle dialectal variation in low-resource settings and explores whether classification outcomes correspond to phonetic proximity and can serve as an exploratory tool for dataset quality. We collected corpora for three macrolanguages Mande, Quechuan, and Yiddish, each presenting distinct internal variation, and evaluated three types of models: GMM, Whisper, and Wav2vec2-based architectures. Models were tested both within language families and across the entire multilingual dataset to assess generalization. Layer-wise classifiers built on wav2vec2-XLSR embeddings were used to identify the layers most sensitive to phonetic or phonological features. Results show that simple GMM models can generalize well in small, highly similar datasets, while Whisper-based classifiers tend to overfit, particularly on closely related dialects. Wav2vec2-XLSR (layer 12 + MLP) captures better fine phonetic and prosodic distinctions, suggesting that embeddings encode nuanced pronunciation cues. For datasets with more diverse sources like Quechua, Whisper demonstrates better generalization. Overall, LID classifiers can both reveal linguistic patterns and highlight dataset quality issues, with model architecture and layer-specific representations shaping performance.