Abstract
This paper demonstrates three different systems capable of performing the multimodal word discovery task. A multimodal word discovery system accepts, as input, a database of spoken descriptions of images (or a set of corresponding phone transcripts), and learns a lexicon which is a mapping from phone strings to their associated image concepts. Three systems are demonstrated: one based on a statistical machine translation (SMT) model, two based on neural machine translation (NMT). On Flickr8k, the SMT-based model performs much better than the NMT-based one, achieving a 49.6% F1 score. Finally, we apply our word discovery system to the task of image retrieval and achieve 29.1% recall@10 on the standard 1000-image Flickr8k tests set.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2683-2687 |
| Number of pages | 5 |
| Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
| Volume | 2019-September |
| DOIs | |
| State | Published - Jan 1 2019 |
| Event | 20th Annual Conference of the International Speech Communication Association: Crossroads of Speech and Language, INTERSPEECH 2019 - Graz, Austria Duration: Sep 15 2019 → Sep 19 2019 |
Keywords
- Multimodal learning
- Neural machine translation
- Statistical machine translation
- Unsupervised spoken word segmentation
ASJC Scopus subject areas
- Language and Linguistics
- Human-Computer Interaction
- Signal Processing
- Software
- Modeling and Simulation