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.

In this early-stage study, we investigate whether transfer learning from lexifier or substrate languages can improve text-to-speech (TTS) performance for low-resource creoles. We conducted a controlled experiment using two creoles of distinct lexical origins: Nigerian Pidgin (English-based) and Guadeloupean Creole (French-based). Single-speaker TTS datasets of approximately 30 minutes each were recorded and used to fine-tune pretrained models for English, French, and Yoruba. Objective metrics and informal subjective evaluations were employed to assess synthesis quality. Though partially inconclusive, our results suggest that the French-based models outperform others for both creoles, while Yoruba-based models yield weaker performance. These findings may suggest that lexical similarity or historic influences alone do not fully predict transfer learning effectiveness, and that phonotactic compatibility and orthographic depth may also be relevant factors. Our work provides insight into TTS model development for creoles and other low-resource languages, and highlights avenues for further research on leveraging relevant linguistic and orthographic features for model development.

This study investigates how large-scale, self-supervised acoustic models (like XLSR and MMS) represent linguistic similarity and whether this can optimize Automatic Speech Recognition (ASR) for low-resource and dialectally diverse languages. While these models excel at cross-lingual transfer learning, their internal representations of fine-grained dialectal variation remain opaque. We focus on Yiddish, a language with a complex dialect continuum, to test if a model’s internal acoustic similarity metric—Acoustic Token Distribution Similarity (ATDS)—predicts ASR performance. Our methodology involved fine-tuning models on Yiddish dialects and measuring ATDS between Yiddish and related languages. Results confirm that ATDS is a meaningful predictor: higher acoustic similarity in the model’s latent space correlates with lower character error rates (CER) after fine-tuning. This relationship is strongest in mid-to-upper layers of the MMS model and for in-domain data. Crucially, ATDS captures model-dependent acoustic similarity, which does not always align with genealogical linguistic relationships but remains a practical indicator of transfer learning potential. We conclude that ATDS is a valuable tool for selecting donor languages to develop more efficient, dialect-sensitive ASR systems for language documentation, even if its absolute values require careful interpretation against linguistic knowledge.