# Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili

arXiv: https://arxiv.org/abs/2306.00410 · HTML: https://mlsanity.com/paper/2306.00410/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 36 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 22 of 22)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training](https://mlsanity.com/paper/2104.01027/) (2021)
- [Rethinking Evaluation in ASR: Are Our Models Robust Enough?](https://mlsanity.com/paper/2010.11745/) (2020)
- [Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencoder](https://mlsanity.com/paper/1603.00982/) (2016)
- [Truly unsupervised acoustic word embeddings using weak top-down constraints in encoder-decoder models](https://mlsanity.com/paper/1811.00403/) (2018)
- [Deep convolutional acoustic word embeddings using word-pair side information](https://mlsanity.com/paper/1510.01032/) (2015)
- [Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches](https://mlsanity.com/paper/1611.02550/) (2016)
- [Query-by-Example Search with Discriminative Neural Acoustic Word Embeddings](https://mlsanity.com/paper/1706.03818/) (2017)
- [End-to-End ASR-free Keyword Search from Speech](https://mlsanity.com/paper/1701.04313/) (2017)
- [Acoustic word embeddings for zero-resource languages using self-supervised contrastive learning and multilingual adaptation](https://mlsanity.com/paper/2103.10731/) (2021)
- [Multilingual Jointly Trained Acoustic and Written Word Embeddings](https://mlsanity.com/paper/2006.14007/) (2020)
- [Semantic query-by-example speech search using visual grounding](https://mlsanity.com/paper/1904.07078/) (2019)
- [Improved acoustic word embeddings for zero-resource languages using multilingual transfer](https://mlsanity.com/paper/2006.02295/) (2020)
- [Neural Network based End-to-End Query by Example Spoken Term Detection](https://mlsanity.com/paper/1911.08332/) (2019)
- [Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical Study](https://mlsanity.com/paper/2106.08686/) (2021)
- [Fast ASR-free and almost zero-resource keyword spotting using DTW and CNNs for humanitarian monitoring](https://mlsanity.com/paper/1806.09374/) (2018)
- [Acoustic span embeddings for multilingual query-by-example search](https://mlsanity.com/paper/2011.11807/) (2020)
- [Multilingual transfer of acoustic word embeddings improves when training on languages related to the target zero-resource language](https://mlsanity.com/paper/2106.12834/) (2021)
- [Feature learning for efficient ASR-free keyword spotting in low-resource languages](https://mlsanity.com/paper/2108.06174/) (2021)

## Built on by (most cited first, 5 of 5)

- [Automatic Speech Recognition for African Low-Resource Languages: Challenges and Future Directions](https://mlsanity.com/paper/2505.11690/) (2025)
- [Improving Acoustic Word Embeddings through Correspondence Training of Self-supervised Speech Representations](https://mlsanity.com/paper/2403.08738/) (2024)
- [Leveraging multilingual transfer for unsupervised semantic acoustic word embeddings](https://mlsanity.com/paper/2307.02083/) (2023)
- [Automatic Speech Recognition (ASR) for African Low-Resource Languages: A Systematic Literature Review](https://mlsanity.com/paper/2510.01145/) (2025)
- [Opportunities and Challenges of Natural Language Processing for Low-Resource Senegalese Languages in Social Science Research](https://mlsanity.com/paper/2601.09716/) (2025)

## Abstract (arXiv)

We consider hate speech detection through keyword spotting on radio broadcasts. One approach is to build an automatic speech recognition (ASR) system for the target low-resource language. We compare this to using acoustic word embedding (AWE) models that map speech segments to a space where matching words have similar vectors. We specifically use a multilingual AWE model trained on labelled data from well-resourced languages to spot keywords in data in the unseen target language. In contrast to ASR, the AWE approach only requires a few keyword exemplars. In controlled experiments on Wolof and Swahili where training and test data are from the same domain, an ASR model trained on just five minutes of data outperforms the AWE approach. But in an in-the-wild test on Swahili radio broadcasts with actual hate speech keywords, the AWE model (using one minute of template data) is more robust, giving similar performance to an ASR system trained on 30 hours of labelled data.