@inproceedings{688578de917a414d9473c52c3f58a606,
title = "Empowering Māori Automatic Speech Recognition through EMD-Based Augmentation",
abstract = "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.",
keywords = "Data Augmentation, Empirical Mode Decomposition (EMD), Low-resource Māori ASR",
author = "Chengxi Lei and Sheng Li and Satwinder Singh and Feng Hou and Huia Jahnke and Ruili Wang",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 22nd Pacific Rim International Conference on Artificial Intelligence, PRICAI 2025 ; Conference date: 17-11-2025 Through 21-11-2025",
year = "2026",
doi = "10.1007/978-981-95-7072-0\_48",
language = "English",
isbn = "9789819570713",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "676--683",
editor = "Yi Mei and Bing Xue and Chao Qian and Quan Bai and Sankalp Khanna",
booktitle = "PRICAI 2025",
address = "Germany",
}