SPEECH-COCO: 600k Visually Grounded Spoken Captions Aligned to MSCOCO Data Set

TL;DR

SPEECH-COCO is a large dataset of over 600,000 visually grounded spoken captions generated from MSCOCO, enabling research in multimodal speech and vision tasks with naturalistic speech signals.

Contribution

This work introduces a new large-scale dataset of spoken image captions with detailed time-aligned annotations, facilitating multimodal speech and vision research.

Findings

Created a dataset with 616,767 spoken captions over 600 hours

Added disfluencies and speed perturbation for naturalness

Demonstrated potential for unsupervised speech pattern discovery

Abstract

This paper presents an augmentation of MSCOCO dataset where speech is added to image and text. Speech captions are generated using text-to-speech (TTS) synthesis resulting in 616,767 spoken captions (more than 600h) paired with images. Disfluencies and speed perturbation are added to the signal in order to sound more natural. Each speech signal (WAV) is paired with a JSON file containing exact timecode for each word/syllable/phoneme in the spoken caption. Such a corpus could be used for Language and Vision (LaVi) tasks including speech input or output instead of text. Investigating multimodal learning schemes for unsupervised speech pattern discovery is also possible with this corpus, as demonstrated by a preliminary study conducted on a subset of the corpus (10h, 10k spoken captions). The dataset is available on Zenodo: https://zenodo.org/record/4282267