mlsanityData to 7 Oct 2026

BembaSpeech: A Speech Recognition Corpus for the Bemba Language

arXiv 2102.04889 ↗First posted 9 Feb 2021cs.CLcited by 14

Not on the map. Why?

Abstract (arXiv)

We present a preprocessed, ready-to-use automatic speech recognition corpus, BembaSpeech, consisting over 24 hours of read speech in the Bemba language, a written but low-resourced language spoken by over 30% of the population in Zambia. To assess its usefulness for training and testing ASR systems for Bemba, we train an end-to-end Bemba ASR system by fine-tuning a pre-trained DeepSpeech English model on the training portion of the BembaSpeech corpus. Our best model achieves a word error rate (WER) of 54.78%. The results show that the corpus can be used for building ASR systems for Bemba. The corpus and models are publicly released at https://github.com/csikasote/BembaSpeech.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 6 of its 50 references that are in the map. First posted on arXiv 9 Feb 2021.

Builds on

6 of 50 references.

References · 6

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  3. 2015Deep Speech 2: End-to-End Speech Recognition in English and Mandarincited by 449
  4. 2014Deep Speech: Scaling up end-to-end speech recognitioncited by 354
  5. 2018The PyTorch-Kaldi Speech Recognition Toolkitcited by 38
  6. 2019Benchmarking Neural Machine Translation for Southern African Languagescited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 4; 2025: 4; 2026: 2.

14 papers.

Sort: