Language learning using Speech to Image retrieval
This work addresses the challenge of enabling computational models to learn language from speech, which is incremental as it builds on existing neural network methods.
The paper tackles the problem of learning language directly from speech without text by improving neural network approaches to create visually grounded embeddings for spoken utterances, resulting in a remarkable increase in image-caption retrieval performance over previous work. It also finds that deeper network layers in the model better encode word presence, showing that the encoder learns to recognize words without explicit training for that task.
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.