@inproceedings{kamahori-etal-2025-liteasr,
    title = "{L}ite{ASR}: Efficient Automatic Speech Recognition with Low-Rank Approximation",
    author = "Kamahori, Keisuke  and
      Kasai, Jungo  and
      Kojima, Noriyuki  and
      Kasikci, Baris",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.169/",
    pages = "3430--3442",
    ISBN = "979-8-89176-332-6",
    abstract = "Modern automatic speech recognition (ASR) models, such as OpenAI{'}s Whisper, rely on deep encoder-decoder architectures, and their encoders are a critical bottleneck for efficient deployment due to high computational intensity. We introduce LiteASR, a low-rank compression scheme for ASR encoders that significantly reduces inference costs while maintaining transcription accuracy. Our approach leverages the strong low-rank properties observed in intermediate activations: by applying principal component analysis (PCA) with a small calibration dataset, we approximate linear transformations with a chain of low-rank matrix multiplications, and further optimize self-attention to work in reduced dimensionality. Evaluation results show that our method can compress Whisper large-v3{'}s encoder size by over 50{\%}, matching Whisper medium{'}s size with better transcription accuracy, thereby establishing a new Pareto frontier of accuracy and efficiency. The code of LiteASR is available at https://github.com/efeslab/LiteASR."
}