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BembaSpeech: A Speech Recognition Corpus for the Bemba Language

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arxiv 2102.04889 v1 pith:ZJUTJGQT submitted 2021-02-09 cs.CL

Claytone Sikasote , Antonios Anastasopoulos This is my paper
classification cs.CL
keywords bembacorpusbembaspeechlanguagespeechmodelrecognitionsystems
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. Breaking the Transcription Bottleneck: Fine-tuning ASR Models for Extremely Low-Resource Fieldwork Languages

    cs.CL 2025-06 conditional novelty 6.0 of 10

    Fine-tuned MMS outperforms XLS-R on fieldwork ASR with less than one hour of training data, while XLS-R reaches parity beyond one hour.

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