  In this work, we propose an effective approach for training unique embedding
representations by combining three simultaneous modalities: image and spoken
and textual narratives. The proposed methodology departs from a baseline system
that spawns a embedding space trained with only spoken narratives and image
cues. Our experiments on the EPIC-Kitchen and Places Audio Caption datasets
show that introducing the human-generated textual transcriptions of the spoken
narratives helps to the training procedure yielding to get better embedding
representations. The triad speech, image and words allows for a better estimate
of the point embedding and show an improving of the performance within tasks
like image and speech retrieval, even when text third modality, text, is not
present in the task.
