Voices of L uxembourg: Tackling Dialect Diversity in a Low-Resource Setting (2025)

Abstract: Dialect classification is essential for preserving linguistic diversity, particularly in low-resource languages such as Luxembourgish. This study introduces one of the first systematic approaches to classifying Luxembourgish dialects, addressing phonetic, prosodic, and lexical variations across four major regions. We benchmarked multiple models, including state-of-the-art pre-trained speech models like Wav2Vec2, XLSR-Wav2Vec2, and Whisper, alongside traditional approaches such as Random Forest and CNN-LSTM. To overcome data limitations, we applied targeted data augmentation strategies and analyzed their impact on model performance. Our findings highlight the superior performance of CNN-Spectrogram and CNN-LSTM models while identifying the strengths and limitations of data augmentation. This work establishes foundational benchmarks and provides actionable insights for advancing dialectal NLP in Luxembourgish and other low-resource languages.

## introduction dialectal research plays a critical role in understanding linguistic diversity and cultural identity. luxembourgish, a west germanic language spoken by over 600,000 people, presents unique challenges due to its regional phonetic, prosodic, and lexical variations. limited annotated resources and influences from german and french complicate automated dialect classification @xcite . luxembourgish dialects are categorized into four regions: north, east, south, and center. each region exhibits distinct linguistic traits, with the northern dialect displaying the most divergence and the central region aligning closely with the standard variety @xcite . automatic dialect classification has practical importance in improving automatic speech recognition (asr) and machine translation systems and in enabling more inclusive digital archiving of dialectal data. previous work has underscored the importance of dialect identification in preserving linguistic diversity and supporting sociolinguistic research @xcite . however, luxembourgish, like other low-resource languages, lacks substantial annotated datasets for automated processing, which hinders the development of robust models for dialect classification. moreover, luxembourgish's multilingual setting presents additional challenges, as shown by existing research in luxembourgish asr and related linguistic tasks @xcite @xcite . ## related work automatic dialect classification has advanced significantly in high-resource languages, where annotated datasets and sophisticated processing tools facilitate robust model performance. for instance, substantial work has been conducted in arabic (harfash and abdul-kareem, 2017), chinese @xcite , german @xcite , and english @xcite . in these languages, the availability of extensive data resources enables classification approaches to take advantage of phonetic, prosodic, and lexical features, supporting higher accuracy and model robustness. for example, harfash and abdul-kareem (2017) improved dialect classification in arabic by incorporating phonetic and prosodic cues, while ng and lee (2008) applied entropy-based measures to enhance chinese dialect classification, highlighting the versatility of feature-based methods in these contexts. in highresource settings, models often use a combination of rule-based linguistic knowledge @xcite and data-driven machine learning techniques that benefit from large training corpora, allowing them to learn complex patterns effectively @xcite . in contrast, low-resource languages like luxembourgish lack the annotated datasets and processing infrastructure needed for accurate dialect classification, presenting unique challenges for computational linguistics. for low-resource languages, researchers have explored strategies such as synthetic data generation and unsupervised learning to mitigate data scarcity. transfer learning, for example, can leverage pre-trained models in related languages, using phonetic similarities to improve dialect classification in underresourced contexts @xcite . data augmentation has also emerged as a critical strategy for low-resource languages, allowing researchers to expand datasets and introduce variability, as demonstrated in tasks involv-ing accent and dialect variation @xcite . for luxembourgish, however, computational research remains relatively limited. existing studies have focused mainly on its phonetic and syntactic characteristics @xcite , as well as distinctive phonological features @xcite , with limited exploration of automated dialect classification. research on regional phonetic variation in luxembourgish indicates that its dialects are influenced by neighboring german and french, with generational shifts contributing further to its linguistic diversity @xcite . this complexity requires tailor-made classifiers and careful feature engineering to capture subtle distinctions in phonetics and prosody that are integral to luxembourgish dialectal variation @xcite . computational studies have suggested that crosslingual models that utilize resources from german and french could improve luxembourgish speech recognition @xcite , highlighting both the potential and the computational challenges that the classification of the luxembourgish dialect entails @xcite . future progress in luxembourgish dialect classification may benefit from techniques like data augmentation, which has proven successful in other low-resource contexts. for instance, @xcite demonstrated that targeted data augmentation techniques, such as pitch and speed modifications, significantly improved the accuracy of dialect classification for chinese dialects, underscoring the value of these methods to improve model performance in low-resource settings. such approaches could potentially be adapted for luxembourgish, where similar variability in phonetic and prosodic features across dialects could benefit from targeted augmentation. building on this foundation, our study introduces a model for the classification of luxembourgish dialects that integrates linguistic insights with computational techniques specifically designed for low-resource settings. by applying data augmentation strategies, we address the constraints imposed by limited annotated data, contributing to the broader field of dialect classification for underrepresented languages. this work aims to lay the groundwork for luxembourgish nlp, underscoring the importance of dialectal research in multilingual nlp and advancing methodologies for low-resource language processing. ## results ## discussion the results demonstrate that data augmentation can contribute to modest but consistent improvements in dialect classification for luxembourgish, a low-resource language. these findings align with prior studies highlighting the effectiveness of cnns and end-to-end asr models, such as wav2vec, in handling spectrogram data for dialect and language classification tasks. cnns have proven effective in extracting meaningful features from spectrograms, which are crucial for distinguishing subtle phonetic and prosodic differences across dialects. for example, @xcite reported that cnns achieved 83% accuracy in arabic dialect classification, capitalizing on their capacity to process spatial information within spectrograms. similarly, @xcite demonstrated cnns' suitability for language identification across multiple languages, achieving up to 89% accuracy by focusing on acoustic cues encoded in spectrograms @xcite . prior studies support our findings, showing that cnn-based models, such as cnn-spectrogram and cnn-lstm, achieved competitive accuracy (68-73%) on luxembourgish dialects, with further improvements post-augmentation. research supports the effectiveness of cnn-lstm architectures in dialect classification, especially for capturing both spatial and temporal linguistic patterns. for instance, cnn-lstm models have shown high accuracy in dialect classification tasks for arabic, where they effectively captured dialectal sentiment variations across regional arabic texts @xcite . similar success has been observed in distinguishing tonal versus non-tonal indian languages using acoustic data, where the model's ability to capture temporal dependencies significantly improved classification outcomes @xcite . studies on dialectal sentiment analysis for roman urdu and english have further highlighted cnn-lstm's adaptability, demonstrating the model's capacity to capture linguistic nuances in dialects within social media contexts @xcite . in general, cnn-lstm hybrids improve dialect classification accuracy by effectively capturing both localized phonetic features and sequential temporal dynamics @xcite . additionally, research on self-supervised models like wav2vec has demonstrated the model's ability to capture detailed phonetic and acoustic features, enabling it to perform well even in low-resource dialect classification tasks. wav2vec embeddings have proven effective in detecting dialect-specific nuances and handling out-ofdistribution dialect data @xcite . studies also show that fine-tuning wav2vec on dialectal datasets enables it to capture phonetic variations in pronunciation, tone, and rhythm, essential for effective dialect classification @xcite . furthermore, wav2vec has demonstrated robustness across low-resource languages, achieving notable improvements in speech recognition for underrepresented dialects @xcite . these findings align with our results, where the cnn-lstm model showed consistent performance gains after augmentation, underscoring the utility of combining convolutional and sequential layers to handle the complex linguistic structures present in luxembourgish dialects. our findings also resonate with prior work in low-resource dialect classification. for instance, in the study by @xcite , a multilingual asr model improved classification accuracy for chinese dialects, significantly reducing classification errors. although we did not directly utilize this approach, wav2vec's pretraining on multilingual datasets may have contributed to its relative robustness in luxembourgish dialect classification. this ability to handle a range of dialectal inputs, even with limited training data, illustrates the model's value in low-resource language contexts. the improvements observed with data augmentation, while modest, highlight its potential to enhance model robustness, particularly for dialects with lower representation. @xcite explored similar strategies by introducing augmented spectrogram features, leading to gains in dialect classification accuracy. in our study, augmenting the dataset by adjusting pitch and tempo introduced additional variability, helping the models to generalize better. this approach was especially beneficial for the random forest model, which lacks the feature extraction capabilities of cnns and asr models. despite the limited scale of the improvements, these findings underscore the utility of data augmentation as a practical approach to mitigate the effects of data scarcity in dialect classification tasks. our cnn-lstm model, designed to capture both spatial and temporal dependencies, also showed consistent gains with augmentation. @xcite demonstrated that wav2vec could maintain robust performance across diverse conditions, including real-world "in-the-wild" settings with noisy and reverberant audio. although the luxembourgish dataset does not include such variability, the slight improvements in recall and precision seen in our cnn-lstm model after augmentation suggest that temporal architectures may add value in dialectal classification tasks, especially in capturing sequential acoustic features. overall, the updated performance metrics reported in ## conclusions we introduced a comprehensive methodology for luxembourgish dialect classification, pairing data augmentation (speed/pitch shifts) with a spectrum of models from random forest to cnn-lstm and pretrained whisper / wav2vec2 variants. our results highlight: cnn-spectrogram achieves top accuracies in northern, southern, and eastern dialects after augmentation, showcasing its spatial feature-extraction strengths. cnn-lstm outperforms other models in central luxembourgish, suggesting the value of modeling temporal dependencies in dialect classification. wav2vec2 remains consistently strong across all dialects, affirming the resilience of self-supervised speech representations. data augmentation partially mitigates imbalance, boosting performance the most in underrepresented dialects (northern and eastern). though the improvements are modest, they demonstrate the potential of augmentation in lowresource dialect classification. future work should explore more advanced augmentation pipelines (e.g., specaugment, multiple pitch/speed factors) and target larger-scale data collection, possibly leveraging multilingual transfer from related germanic varieties. these steps will be instrumental in achieving broader robustness and higher accuracy for luxembourgish and other low-resource dialects.