Cross-Lingual Query-by-Example Spoken Term Detection: A Transformer-Based Approach

• URL: http://arxiv.org/abs/2410.04091v1
• Date: Sat, 5 Oct 2024 09:19:29 GMT
• Title: Cross-Lingual Query-by-Example Spoken Term Detection: A Transformer-Based Approach
• Authors: Allahdadi Fatemeh, Mahdian Toroghi Rahil, Zareian Hassan,
• Abstract summary: This paper introduces a novel, language-agnostic QbE-STD model leveraging image processing techniques and transformer architecture. Experimental results across four languages demonstrate significant performance gains (19-54%) over a CNN-based baseline.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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