Cross-Lingual Query-by-Example Spoken Term Detection: A Transformer-Based Approach
<|reference_start|>Cross-Lingual Query-by-Example Spoken Term Detection: A Transformer-Based Approach: Query-by-example spoken term detection (QbE-STD) is typically constrained by transcribed data scarcity and language specificity. This paper introduces a novel, language-agnostic QbE-STD model leveraging image processing techniques and transformer architecture. By employing a pre-trained XLSR-53 network for feature extraction and a Hough transform for detection, our model effectively searches for user-defined spoken terms within any audio file. Experimental results across four languages demonstrate significant performance gains (19-54%) over a CNN-based baseline. While processing time is improved compared to DTW, accuracy remains inferior. Notably, our model offers the advantage of accurately counting query term repetitions within the target audio.<|reference_end|>
@article{fatemeh2024cross-lingual,
    title={Cross-Lingual Query-by-Example Spoken Term Detection: A
  Transformer-Based Approach},
    author={Allahdadi Fatemeh, Mahdian Toroghi Rahil, Zareian Hassan},
    journal={arXiv preprint arXiv:2410.04091},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04091},
    primaryClass={cs.LG cs.SD eess.AS}
}