Language learning using Speech to Image retrieval

TL;DR

This paper presents a neural network approach that learns visually grounded embeddings from speech for image-caption retrieval, demonstrating improved performance and emergent word recognition capabilities without explicit training.

Contribution

It introduces a novel neural architecture combining GRUs, attention, and ensembling to learn from speech directly, advancing speech-based image retrieval.

Findings

Significant improvement in image-caption retrieval accuracy.

Deeper layers better encode word presence.

The model learns word recognition without explicit training.

Abstract

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.