{"id":32311,"date":"2026-01-21T17:01:41","date_gmt":"2026-01-21T17:01:41","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/does-preprocessing-matter-an-analysis-of-acoustic-feature-importance-in-deep-learning-for-dialect-classification\/"},"modified":"2026-06-08T13:19:48","modified_gmt":"2026-06-08T13:19:48","slug":"does-preprocessing-matter-an-analysis-of-acoustic-feature-importance-in-deep-learning-for-dialect-classification","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/does-preprocessing-matter-an-analysis-of-acoustic-feature-importance-in-deep-learning-for-dialect-classification\/","title":{"rendered":"Does Preprocessing Matter? An Analysis of Acoustic Feature Importance in Deep Learning for Dialect Classification"},"content":{"rendered":"<p>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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[32],"class_list":["post-32311","publication","type-publication","status-publish","hentry","publication-type-inproceedings"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32311","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/12"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32311\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32311"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32311"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}