# Semantic query-by-example speech search using visual grounding

arXiv: https://arxiv.org/abs/1904.07078 · HTML: https://mlsanity.com/paper/1904.07078/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

## Built on by (most cited first, 11 of 11)

- [Visually grounded models of spoken language: A survey of datasets, architectures and evaluation techniques](https://mlsanity.com/paper/2104.13225/) (2021)
- [CSFCube -- A Test Collection of Computer Science Research Articles for Faceted Query by Example](https://mlsanity.com/paper/2103.12906/) (2021)
- [Attention-Based Keyword Localisation in Speech using Visual Grounding](https://mlsanity.com/paper/2106.08859/) (2021)
- [Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Model](https://mlsanity.com/paper/2305.11435/) (2023)
- [Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme Discovery](https://mlsanity.com/paper/2103.08199/) (2021)
- [Multilingual transfer of acoustic word embeddings improves when training on languages related to the target zero-resource language](https://mlsanity.com/paper/2106.12834/) (2021)
- [Keyword localisation in untranscribed speech using visually grounded speech models](https://mlsanity.com/paper/2202.01107/) (2022)
- [Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili](https://mlsanity.com/paper/2306.00410/) (2023)
- [Hindi as a Second Language: Improving Visually Grounded Speech with Semantically Similar Samples](https://mlsanity.com/paper/2303.17517/) (2023)
- [Visually grounded few-shot word learning in low-resource settings](https://mlsanity.com/paper/2306.11371/) (2023)
- [Visually grounded few-shot word acquisition with fewer shots](https://mlsanity.com/paper/2305.15937/) (2023)

## Abstract (arXiv)

A number of recent studies have started to investigate how speech systems can be trained on untranscribed speech by leveraging accompanying images at training time. Examples of tasks include keyword prediction and within- and across-mode retrieval. Here we consider how such models can be used for query-by-example (QbE) search, the task of retrieving utterances relevant to a given spoken query. We are particularly interested in semantic QbE, where the task is not only to retrieve utterances containing exact instances of the query, but also utterances whose meaning is relevant to the query. We follow a segmental QbE approach where variable-duration speech segments (queries, search utterances) are mapped to fixed-dimensional embedding vectors. We show that a QbE system using an embedding function trained on visually grounded speech data outperforms a purely acoustic QbE system in terms of both exact and semantic retrieval performance.