Automatic Speech Recognition for the Ika Language

• URL: http://arxiv.org/abs/2410.00940v1
• Date: Tue, 1 Oct 2024 11:56:42 GMT
• Title: Automatic Speech Recognition for the Ika Language
• Authors: Uchenna Nzenwata, Daniel Ogbuigwe,
• Abstract summary: We fine-tune the pretrained wav2vec 2.0 Massively translations Speech Models on a high-quality speech dataset compiled from New Testament Bible Multilingual in Ika. Our results show that fine-tuning multilingual pretrained models achieves a Word Error Rate (WER) of 0.5377 and Character Error Rate (CER) of 0.2651 with just over 1 hour of training data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a cost-effective approach for developing Automatic Speech Recognition (ASR) models for low-resource languages like Ika. We fine-tune the pretrained wav2vec 2.0 Massively Multilingual Speech Models on a high-quality speech dataset compiled from New Testament Bible translations in Ika. Our results show that fine-tuning multilingual pretrained models achieves a Word Error Rate (WER) of 0.5377 and Character Error Rate (CER) of 0.2651 with just over 1 hour of training data. The larger 1 billion parameter model outperforms the smaller 300 million parameter model due to its greater complexity and ability to store richer speech representations. However, we observe overfitting to the small training dataset, reducing generalizability. Our findings demonstrate the potential of leveraging multilingual pretrained models for low-resource languages. Future work should focus on expanding the dataset and exploring techniques to mitigate overfitting.

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