Does Preprocessing Matter? An Analysis of Acoustic Feature Importance in Deep Learning for Dialect Classification (2025)

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.

## introduction in the realm of deep learning, preprocessing plays a crucial role in optimizing model performance. while many studies focus on text, like (uysal and gunal, 2014), others concentrate on environmental sound classification (esc) or automatic speech recognition (asr) @xcite . for instance, @xcite are exploring existing papers on preprocessing for esc. additionally, some studies focus on using spectrograms for audio processing @xcite . moreover, some research has attempted to enhance speech recordings for dialect identification, leading to improved subjective quality @xcite . however, these studies did not evaluate whether such preprocessing techniques actually improve the performance in downstream tasks. furthermore, despite studies such as @xcite , which incorporate noise reduction as a preprocessing step for dialect identification, a comprehensive study on the key aspects of audio preprocessing for dialect identification remains lacking. often, only individual aspects of preprocessing are considered, as seen in @xcite , where vocal tract length normalization (vtln) and speech rate normalization (srn) are examined. large-scale systems such as whisper @xcite ) and meta's massively multilingual speech (mms) project @xcite highlight the power of extensive and diverse datasets in advancing asr and language identification. whisper, trained on 680,000 hours of multilingual and multitask supervised data, achieves improved robustness to accents, background noise, and technical language, demonstrating the impact of its large dataset. similarly, meta's mms project tackles the lack of asr systems for many languages by using religious texts, translated into numerous languages, to build a diverse training dataset. these projects showcase the importance of large datasets for robustness and inclusivity. in contrast, this study addresses the challenges of working with smaller, constrained datasets. notably, no paper has been found that investigates the effects of preprocessing raw audio on language or dialect classification. this gap is particularly significant in the context of deep learning-based dialect identification (did), where understanding the fundamental aspects of audio preprocessing tailored specifically for dialect classification remains under-explored. this issue resonates with findings in music information retrieval research, where deep learning efforts often prioritize optimizing hyperparameters that define network structure, while the audio preprocessing stage is often not optimized @xcite . this study aims to bridge this gap by investigating how preprocessing adjustments affect the performance of dialect classification models trained on german audio data. we concentrate on the raw waveform and underscore the importance of different audio features in dialect classification. specifically, we aim to determine whether adapting audio inputs improves model performance and whether certain features are misleading for the model, causing it to learn irrelevant patterns. additionally, we explore if deep learning models inherently learn to ignore such variations or if performance even worsens, indicating that these features are important for dialect recognition in german. our contributions are threefold: ## used acoustic features used acoustic features are root mean square (rms) amplitude, dc-offset, articulation rate (ar), pitch, and harmonics-to-noise ratio (hnr). rms amplitude of a digital audio signal represents its perceived loudness and is simultaneously the mean absolute value of the signal. while rms measures the average power of a signal, intensity in decibels (db) quantifies the power relative to a reference level, typically the threshold of human hearing, on a logarithmic scale. as these metrics are correlated, only rms is considered in this study. rms amplitude reflects both the speaker's vocal effort and external factors such as the recording equipment and the recording environment, including background noise and microphone distance. dc-offset (also known as dc-bias), determined by the average amplitude of a segment of the signal, indicates a deviation from the symmetrical nature of a normal voice signal. in a typical symmetric sine signal, the high peak equals the low peak, resulting in an average value near zero over time. however, when a dc offset is present, the symmetry is disrupted, and the average value deviates from zero foot_0 . despite being imperceptible, it reduces the available dynamic range, limiting the signals amplitude variation. dc-offset is primarily influenced by the recording equipment rather than the speaker. articulation rate (ar) measures syllables per second during speech, excluding pauses, whereas speech rate (sr) includes pauses in its calculation. in this study, ar is emphasized over sr, as the audio data has been preprocessed to exclude pauses and non-articulatory elements. also @xcite states that variations in articulation speed between speakers may be more indicative of individual speaking styles than differences in overall speech tempo. the ar regarding to regional distribution has been minimally investigated thus far. @xcite found that there are differences in ar, but also suggest that this may correlate with other processes such as the elision of segments. they conclude that there must be different sound duration ratios in the different regions. pitch, often referred to synonymously as f0, stands for the fundamental frequency of a sound wave. f0 refers to the physical oscillation, while pitch denotes the perceived tonal height of the sound. in tools such as praat @xcite , the pitch refers to f0. pitch normalization, akin to vocal tract length normalization (vtln), aims to mitigate speaker-specific variations in speech signals attributed to differences in vocal tract lengths, which are influenced by physiological factors such as sex. in explor-ing the connection between pitch and dialects, it's noteworthy that the typical fundamental frequency doesn't always align directly with dialect variations. instead, phenomena such as variations in voice quality due to dialectal influences can affect pitch. the harmonics-to-noise ratio (hnr) quantifies the relationship between periodic components and noise in a signal. it measures acoustic periodicity by comparing the energy of harmonics to that of noise, with the result expressed in decibels (db), indicating the dominance of periodic components over noise: an hnr of 20 db signifies 99% of energy in periodic components and 1% in noise, calculated as 10 * log10(99/1). an hnr of 0 db indicates equal energy distribution between harmonics and noise foot_1 . in speech analysis, hnr is favored over signal-to-noise ratio (snr) for its ability to capture voiced sounds periodicity. hnr primarily reflects characteristics of the speaker's voice, such as vocal cord vibration regularity and voice quality, but can also be affected by the recording equipment and environmental noise. ## results ## discussion the study's results indicate that pitch variation did not impact model performance among the used group of older males, suggesting its potential applicability across different age groups. however, it's uncertain if pitch normalization would have the same effect in a diverse group, where sex and age may introduce more pitch variation. future studies should explore the impact of pitch normalization on mixed demographics and evaluate broader techniques such as voice conversion techniques to standardize all audio inputs. although the analysis focused on a german dialect dataset, these insights could extend to other corpora. nonetheless, it is essential to conduct a thorough evaluation of each dataset's features to ensure that preprocessing techniques are well-suited to its specific characteristics and contribute to the classification tasks coherence and relevance. the precise feature extraction values may vary depending on the extraction methods and parameters used. however, rms and dc-offset measurements should consistently yield the same results, as these values can be accurately calculated. in contrast, when extracting pitch features, parameters such as the pitch floor and ceiling should be adjusted according to the age and sex of the speakers to obtain more accurate estimations of the true pitch.