ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset

Ian Palmer, Andrew Rouditchenko, Andrei Barbu, Boris Katz, James Glass

Visually-grounded spoken language datasets can enable models to learn cross-modal correspondences with very weak supervision. However, modern audio-visual datasets contain biases that undermine the real-world performance of models trained on that data. We introduce Spoken ObjectNet, which is designed to remove some of these biases and provide a way to better evaluate how effectively models will perform in real-world scenarios. This dataset expands upon ObjectNet, which is a bias-controlled image dataset that features similar image classes to those present in ImageNet.

We detail our data collection pipeline, which features several methods to improve caption quality, including automated language model checks. Lastly, we show baseline results on image retrieval and audio retrieval tasks. These results show that models trained on other datasets and then evaluated on Spoken ObjectNet tend to perform poorly due to biases in other datasets that the models have learned. We also show evidence that the performance decrease is due to the dataset controls, and not the transfer setting.

doi: 10.21437/Interspeech.2021-245

Cite as: Palmer, I., Rouditchenko, A., Barbu, A., Katz, B., Glass, J. (2021) Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset. Proc. Interspeech 2021, 3650-3654, doi: 10.21437/Interspeech.2021-245

  author={Ian Palmer and Andrew Rouditchenko and Andrei Barbu and Boris Katz and James Glass},
  title={{Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset}},
  booktitle={Proc. Interspeech 2021},