This paper examines the effect of preprocessing techniques on spoken dialect classification using raw audio data. We focus on modifying Root Mean Square (RMS) amplitude, DC-offset, articulation rate (AR), pitch, and Harmonics-to-Noise Ratio (HNR) to assess their impact on model performance. Our analysis determines whether these features are important, irrelevant, or misleading for the classification task. To evaluate these effects, we use a pipeline that tests the significance of each acoustic feature through distortion and normalization techniques. While preprocessing did not directly improve classification accuracy, our findings reveal three key insights: deep learning models for dialect classification are generally robust to variations in the tested audio features, suggesting that normalization may not be necessary. We identify articulation rate as a critical factor, directly affecting the amount of information in audio chunks. Additionally, we demonstrate that intonation, specifically the pitch range, plays a vital role in dialect recognition.

We investigate lightweight and easily applicable data augmentation techniques for dialectal audio classification. We evaluate four main methods, namely shifting pitch, interval removal, background noise insertion and interval swap as well as several subvariants on recordings from 20 German dialects. Each main method is tested across multiple hyperparameter combinations, inlcuding augmentation length, coverage ratio and number of augmentations per original sample. Our results show that frequency-based techniques, particularly frequency masking, consistently yield performance improvements, while others such as time masking or speaker-based insertion can negatively affect the results. Our comparative analysis identifies which augmentations are most effective under realistic conditions, offering simple and efficient strategies to improve dialectal speech classification.

Based on historical dialect data we introduce a local measure of linguistic coherence in spatial language variation aiming at the identification of regions which are particularly sensitive to language variation and change. Besides, we use a measure of global coherence for the automated detection of linguistic items (e.g., sounds or morphemes) with higher or lesser language variation. The paper describes both the data and the method and provides analyses examples.