# Multilingual Bottleneck Features for Query by Example Spoken Term Detection

arXiv: https://arxiv.org/abs/1907.00443 · HTML: https://mlsanity.com/paper/1907.00443/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [CNN-based Spoken Term Detection and Localization without Dynamic Programming](https://mlsanity.com/paper/2103.05468/) (2021)
- [Cross-Lingual Query-by-Example Spoken Term Detection: A Transformer-Based Approach](https://mlsanity.com/paper/2410.04091/) (2024)

## Abstract (arXiv)

State of the art solutions to query by example spoken term detection (QbE-STD) usually rely on bottleneck feature representation of the query and audio document to perform dynamic time warping (DTW) based template matching. Here, we present a study on QbE-STD performance using several monolingual as well as multilingual bottleneck features extracted from feed forward networks. Then, we propose to employ residual networks (ResNet) to estimate the bottleneck features and show significant improvements over the corresponding feed forward network based features. The neural networks are trained on GlobalPhone corpus and QbE-STD experiments are performed on a very challenging QUESST 2014 database.