# The evaluation of a code-switched Sepedi-English automatic speech recognition system

arXiv: https://arxiv.org/abs/2403.07947 · HTML: https://mlsanity.com/paper/2403.07947/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Intermediate Loss Regularization for CTC-based Speech Recognition](https://mlsanity.com/paper/2102.03216/) (2021)
- [Low-resource Languages: A Review of Past Work and Future Challenges](https://mlsanity.com/paper/2006.07264/) (2020)
- [Transformer ASR with Contextual Block Processing](https://mlsanity.com/paper/1910.07204/) (2019)
- [Towards End-to-End Code-Switching Speech Recognition](https://mlsanity.com/paper/1810.13091/) (2018)
- [Improving CTC-based speech recognition via knowledge transferring from pre-trained language models](https://mlsanity.com/paper/2203.03582/) (2022)
- [Canonical and Surface Morphological Segmentation for Nguni Languages](https://mlsanity.com/paper/2104.00767/) (2021)
- [Low-Resource Language Modelling of South African Languages](https://mlsanity.com/paper/2104.00772/) (2021)
- [Semi-supervised acoustic model training for five-lingual code-switched ASR](https://mlsanity.com/paper/1906.08647/) (2019)
- [Minimum word error training for non-autoregressive Transformer-based code-switching ASR](https://mlsanity.com/paper/2110.03573/) (2021)
- [Semi-supervised Development of ASR Systems for Multilingual Code-switched Speech in Under-resourced Languages](https://mlsanity.com/paper/2003.03135/) (2020)

## Abstract (arXiv)

Speech technology is a field that encompasses various techniques and tools used to enable machines to interact with speech, such as automatic speech recognition (ASR), spoken dialog systems, and others, allowing a device to capture spoken words through a microphone from a human speaker. End-to-end approaches such as Connectionist Temporal Classification (CTC) and attention-based methods are the most used for the development of ASR systems. However, these techniques were commonly used for research and development for many high-resourced languages with large amounts of speech data for training and evaluation, leaving low-resource languages relatively underdeveloped. While the CTC method has been successfully used for other languages, its effectiveness for the Sepedi language remains uncertain. In this study, we present the evaluation of the Sepedi-English code-switched automatic speech recognition system. This end-to-end system was developed using the Sepedi Prompted Code Switching corpus and the CTC approach. The performance of the system was evaluated using both the NCHLT Sepedi test corpus and the Sepedi Prompted Code Switching corpus. The model produced the lowest WER of 41.9%, however, the model faced challenges in recognizing the Sepedi only text.