Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset
| Autor: | Andrei Barbu, James Glass, Andrew Rouditchenko, Ian Palmer, Boris Katz |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer science Computer Vision and Pattern Recognition (cs.CV) media_common.quotation_subject Computer Science - Computer Vision and Pattern Recognition computer.software_genre Image (mathematics) Audio and Speech Processing (eess.AS) FOS: Electrical engineering electronic engineering information engineering Quality (business) Baseline (configuration management) Image retrieval media_common Data collection Computer Science - Computation and Language business.industry Pipeline (software) Multimedia (cs.MM) Language model Artificial intelligence business Computation and Language (cs.CL) computer Natural language processing Computer Science - Multimedia Spoken language Electrical Engineering and Systems Science - Audio and Speech Processing |
| Popis: | 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. Presented at Interspeech 2021. This version contains additional experiments on the Spoken ObjectNet test set |
| Databáze: | OpenAIRE |
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