@inproceedings{fischbach-etal-2025-preprocessing,
    title = "Does Preprocessing Matter? {An} Analysis of Acoustic Feature Importance in Deep Learning for Dialect Classification",
    author = "Fischbach, Lea  and
      Kleen, Caroline  and
      Flek, Lucie  and
      Lameli, Alfred",
    editor = "Johansson, Richard  and
      Stymne, Sara",
    booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
    month = mar,
    year = "2025",
    address = "Tallinn, Estonia",
    publisher = "University of Tartu Library",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.nodalida-1.16/",
    pages = "159--169",
    ISBN = "978-9908-53-109-0",
    abstract = "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."
}