BembaSpeech: A Speech Recognition Corpus for the Bemba Language

2021-02-09 · LREC 2022 6 · Claytone Sikasote, Antonios Anastasopoulos

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.

📄 PDF Abstract BibTeX arXiv:2102.04889

Code (2)

csikasote/BembaASR 공식 구현

csikasote/BembaSpeech 공식 구현

Tasks

Automatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognition Speech Recognition

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Machine Translation speech-recognition Speech Recognition Translation

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Data Augmentation Translation

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Automatic Speech Recognition Automatic Speech Recognition (ASR)Machine Translation speech-recognition+4

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Automatic Speech Recognition Automatic Speech Recognition (ASR)Language Identification speech-recognition+3

Huqariq: A Multilingual Speech Corpus of Native Languages of Peru for Speech Recognition

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