# Language learning using Speech to Image retrieval

arXiv: https://arxiv.org/abs/1909.03795 · HTML: https://mlsanity.com/paper/1909.03795/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2019.

## Built on by (most cited first, 21 of 21)

- [Self-Supervised Speech Representation Learning: A Review](https://mlsanity.com/paper/2205.10643/) (2022)
- [Spoken Moments: Learning Joint Audio-Visual Representations from Video Descriptions](https://mlsanity.com/paper/2105.04489/) (2021)
- [Visually grounded models of spoken language: A survey of datasets, architectures and evaluation techniques](https://mlsanity.com/paper/2104.13225/) (2021)
- [Self-Supervised Representation Learning for Speech Using Visual Grounding and Masked Language Modeling](https://mlsanity.com/paper/2202.03543/) (2022)
- [Fast-Slow Transformer for Visually Grounding Speech](https://mlsanity.com/paper/2109.08186/) (2021)
- [Talk, Don't Write: A Study of Direct Speech-Based Image Retrieval](https://mlsanity.com/paper/2104.01894/) (2021)
- [Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? -- A computational investigation](https://mlsanity.com/paper/2109.14200/) (2021)
- [Evaluation of Audio-Visual Alignments in Visually Grounded Speech Models](https://mlsanity.com/paper/2108.02562/) (2021)
- [Cascaded Multilingual Audio-Visual Learning from Videos](https://mlsanity.com/paper/2111.04823/) (2021)
- [Learning English with Peppa Pig](https://mlsanity.com/paper/2202.12917/) (2022)
- [ConceptBeam: Concept Driven Target Speech Extraction](https://mlsanity.com/paper/2207.11964/) (2022)
- [ZR-2021VG: Zero-Resource Speech Challenge, Visually-Grounded Language Modelling track, 2021 edition](https://mlsanity.com/paper/2107.06546/) (2021)
- [Semantic sentence similarity: size does not always matter](https://mlsanity.com/paper/2106.08648/) (2021)
- [InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for Images](https://mlsanity.com/paper/2211.12760/) (2022)
- [On the robustness of modeling grounded word learning through a child's egocentric input](https://mlsanity.com/paper/2507.14749/) (2025)
- [Discrete representations in neural models of spoken language](https://mlsanity.com/paper/2105.05582/) (2021)
- [Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset](https://mlsanity.com/paper/2110.07575/) (2021)
- [A Survey of Recent Advances and Challenges in Deep Audio-Visual Correlation Learning](https://mlsanity.com/paper/2412.00049/) (2024)
- [Computational modeling of early language learning from acoustic speech and audiovisual input without linguistic priors](https://mlsanity.com/paper/2603.08359/) (2026)
- [Recent Advances and Challenges in Deep Audio-Visual Correlation Learning](https://mlsanity.com/paper/2202.13673/) (2022)
- [Modelling word learning and recognition using visually grounded speech](https://mlsanity.com/paper/2203.06937/) (2022)

## Abstract (arXiv)

Humans learn language by interaction with their environment and listening to other humans. It should also be possible for computational models to learn language directly from speech but so far most approaches require text. We improve on existing neural network approaches to create visually grounded embeddings for spoken utterances. Using a combination of a multi-layer GRU, importance sampling, cyclic learning rates, ensembling and vectorial self-attention our results show a remarkable increase in image-caption retrieval performance over previous work. Furthermore, we investigate which layers in the model learn to recognise words in the input. We find that deeper network layers are better at encoding word presence, although the final layer has slightly lower performance. This shows that our visually grounded sentence encoder learns to recognise words from the input even though it is not explicitly trained for word recognition.