# Phonetic Word Embeddings

arXiv: https://arxiv.org/abs/2109.14796 · HTML: https://mlsanity.com/paper/2109.14796/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 30 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Structural Analysis of Hindi Phonetics and A Method for Extraction of Phonetically Rich Sentences from a Very Large Hindi Text Corpus](https://mlsanity.com/paper/1701.08655/) (2017)
- [Faster Population Counts Using AVX2 Instructions](https://mlsanity.com/paper/1611.07612/) (2016)

## Built on by (most cited first, 7 of 7)

- [PWESuite: Phonetic Word Embeddings and Tasks They Facilitate](https://mlsanity.com/paper/2304.02541/) (2023)
- [Transforming NLU with Babylon: A Case Study in Development of Real-time, Edge-Efficient, Multi-Intent Translation System for Automated Drive-Thru Ordering](https://mlsanity.com/paper/2411.15372/) (2024)
- [Pun Intended: Multi-Agent Translation of Wordplay with Contrastive Learning and Phonetic-Semantic Embeddings for CLEF JOKER 2025 Task 2](https://mlsanity.com/paper/2507.06506/) (2025)
- [No More Mumbles: Enhancing Robot Intelligibility through Speech Adaptation](https://mlsanity.com/paper/2405.09708/) (2024)
- [The Cognate Data Bottleneck in Language Phylogenetics](https://mlsanity.com/paper/2507.00911/) (2025)
- [Searching for Sound-Meaning Collisions: Graph-Based Affordance Retrieval and Multi-Evaluator Ranking for Pun Translation at CLEF 2026 JOKER Task 2](https://mlsanity.com/paper/2608.04299/) (2026)
- [Pun Intended: Multi-Agent Translation of Wordplay with Contrastive Learning and Phonetic-Semantic Embeddings](https://mlsanity.com/paper/2608.04311/) (2026)

## Abstract (arXiv)

This work presents a novel methodology for calculating the phonetic similarity between words taking motivation from the human perception of sounds. This metric is employed to learn a continuous vector embedding space that groups similar sounding words together and can be used for various downstream computational phonology tasks. The efficacy of the method is presented for two different languages (English, Hindi) and performance gains over previous reported works are discussed on established tests for predicting phonetic similarity. To address limited benchmarking mechanisms in this field, we also introduce a heterographic pun dataset based evaluation methodology to compare the effectiveness of acoustic similarity algorithms. Further, a visualization of the embedding space is presented with a discussion on the various possible use-cases of this novel algorithm. An open-source implementation is also shared to aid reproducibility and enable adoption in related tasks.