# Automatic Speech Recognition for Humanitarian Applications in Somali

arXiv: https://arxiv.org/abs/1807.08669 · HTML: https://mlsanity.com/paper/1807.08669/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2018.

## Built on by (most cited first, 2 of 2)

- [The Makerere Radio Speech Corpus: A Luganda Radio Corpus for Automatic Speech Recognition](https://mlsanity.com/paper/2206.09790/) (2022)
- [How much speech data is necessary for ASR in African languages? An evaluation of data scaling in Kinyarwanda and Kikuyu](https://mlsanity.com/paper/2510.07221/) (2025)

## Abstract (arXiv)

We present our first efforts in building an automatic speech recognition system for Somali, an under-resourced language, using 1.57 hrs of annotated speech for acoustic model training. The system is part of an ongoing effort by the United Nations (UN) to implement keyword spotting systems supporting humanitarian relief programmes in parts of Africa where languages are severely under-resourced. We evaluate several types of acoustic model, including recent neural architectures. Language model data augmentation using a combination of recurrent neural networks (RNN) and long short-term memory neural networks (LSTMs) as well as the perturbation of acoustic data are also considered. We find that both types of data augmentation are beneficial to performance, with our best system using a combination of convolutional neural networks (CNNs), time-delay neural networks (TDNNs) and bi-directional long short term memory (BLSTMs) to achieve a word error rate of 53.75%.