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In this work, we propose an effective approach for training unique embeddingrepresentations by combining three simultaneous modalities: image and spokenand textual narratives. The proposed methodology departs from a baseline systemthat spawns a embedding space trained with only spoken narratives and imagecues. Our experiments on the EPIC-Kitchen and Places Audio Caption datasetsshow that introducing the human-generated textual transcriptions of the spokennarratives helps to the training procedure yielding to get better embeddingrepresentations. The triad speech, image and words allows for a better estimateof the point embedding and show an improving of the performance within taskslike image and speech retrieval, even when text third modality, text, is notpresent in the task.

Transcription-Enriched Joint Embeddings for Spoken Descriptions of Images and Videos