Low-resource languages like Māori face significant challenges in developing robust Automatic Speech Recognition (ASR) systems due to limited annotated data and linguistic resources. This paper proposes a novel data augmentation framework that enriches training data for ASR models through Empirical Mode Decomposition (EMD) based frequency band perturbation. EMD is employed to decompose speech signals into intrinsic mode functions (IMFs), enabling selective removal of specific frequency components to simulate variations in speaker traits and acoustic environments. Experiments on a self-collected 17-hour Māori speech corpus demonstrate consistent improvements across three ASR architectures, including DeepSpeech, Wav2Vec 2.0 XLS-R, and HuBERT. The proposed method significantly reduces Word Error Rates (WER), especially when combined with SpecAugment, underscoring its complementary benefits and effectiveness in enhancing generalization for Māori ASR.