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.

本研究针对低资源语言伊卡语，提出了一种成本效益高的自动语音识别(ASR)模型开发方法。通过对预训练的多语种wav2vec 2.0模型进行微调，结果表明该模型在仅用1小时训练数据的情况下取得了0.5377的字错误率和0.2651的字符错误率，展示了利用多语言预训练模型对低资源语言的潜力。

伊卡语的自动语音识别