# Sound-Word2Vec: Learning Word Representations Grounded in Sounds

arXiv: https://arxiv.org/abs/1703.01720 · HTML: https://mlsanity.com/paper/1703.01720/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2017.

## Built on by (most cited first, 4 of 4)

- [Grounding 'Grounding' in NLP](https://mlsanity.com/paper/2106.02192/) (2021)
- [Impressions2Font: Generating Fonts by Specifying Impressions](https://mlsanity.com/paper/2103.10036/) (2021)
- [Theoretical foundations and limits of word embeddings: what types of meaning can they capture?](https://mlsanity.com/paper/2107.10413/) (2021)
- [Using Pause Information for More Accurate Entity Recognition](https://mlsanity.com/paper/2109.13222/) (2021)

## Abstract (arXiv)

To be able to interact better with humans, it is crucial for machines to understand sound - a primary modality of human perception. Previous works have used sound to learn embeddings for improved generic textual similarity assessment. In this work, we treat sound as a first-class citizen, studying downstream textual tasks which require aural grounding. To this end, we propose sound-word2vec - a new embedding scheme that learns specialized word embeddings grounded in sounds. For example, we learn that two seemingly (semantically) unrelated concepts, like leaves and paper are similar due to the similar rustling sounds they make. Our embeddings prove useful in textual tasks requiring aural reasoning like text-based sound retrieval and discovering foley sound effects (used in movies). Moreover, our embedding space captures interesting dependencies between words and onomatopoeia and outperforms prior work on aurally-relevant word relatedness datasets such as AMEN and ASLex.