Cross-Lingual Query-by-Example Spoken Term Detection: A Transformer-Based Approach
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 37 references that are in the map. First posted on arXiv 5 Oct 2024.
Builds on
8 of 37 references.
References · 8
- 2020Unsupervised Cross-lingual Representation Learning for Speech Recognitioncited by 496
- 2015Deep convolutional acoustic word embeddings using word-pair side informationcited by 27
- 2016Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approachescited by 25
- 2017Query-by-Example Search with Discriminative Neural Acoustic Word Embeddingscited by 22
- 2019Neural Network based End-to-End Query by Example Spoken Term Detectioncited by 8
- 2020Acoustic span embeddings for multilingual query-by-example searchcited by 7
- 2017Query-by-example Spoken Term Detection using Attention-based Multi-hop Networkscited by 4
- 2019Multilingual Bottleneck Features for Query by Example Spoken Term Detectioncited by 2
Built on by
Not cited yet.