Summary of Cross-lingual Query-by-example Spoken Term Detection: a Transformer-based Approach, by Allahdadi Fatemeh et al.
Cross-Lingual Query-by-Example Spoken Term Detection: A Transformer-Based Approach
by Allahdadi Fatemeh, Mahdian Toroghi Rahil, Zareian Hassan
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Sound (cs.SD); Audio and Speech Processing (eess.AS)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel Query-by-example Spoken Term Detection (QbE-STD) model introduced in this paper leverages image processing techniques and transformer architecture to efficiently search for user-defined spoken terms within any audio file, transcending language barriers. By employing a pre-trained XLSR-53 network for feature extraction and a Hough transform for detection, the model demonstrates significant performance gains (19-54%) over a CNN-based baseline across four languages. Although processing time is improved compared to DTW, accuracy remains inferior. The model’s key advantage lies in its ability to accurately count query term repetitions within the target audio. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to find specific words or phrases spoken in different languages. This paper introduces a new way to do that using special techniques from image processing and machine learning. It’s called Query-by-example Spoken Term Detection, or QbE-STD for short. The idea is to use computers to search through audio files and find the words or phrases you’re looking for. This can be helpful in lots of situations, like understanding what people are saying in different languages. The new method is tested on four different languages and shows big improvements over older methods. |
Keywords
» Artificial intelligence » Cnn » Feature extraction » Machine learning » Transformer
