Despite recent availability of large transcribed Kinyarwanda speech data, achieving robust speech recognition for Kinyarwanda is still challenging. In this work, we show that using self-supervised pre-training, following a simple curriculum schedule during fine-tuning and using semi-supervised learning to leverage large unlabelled speech data significantly improve speech recognition performance for Kinyarwanda. Our approach focuses on using public domain data only. A new studio-quality speech dataset is collected from a public website, then used to train a clean baseline model. The clean baseline model is then used to rank examples from a more diverse and noisy public dataset, defining a simple curriculum training schedule. Finally, we apply semi-supervised learning to label and learn from large unlabelled data in five successive generations. Our final model achieves 3.2% word error rate (WER) on the new dataset and 15.6% WER on Mozilla Common Voice benchmark, which is state-of-the-art to the best of our knowledge. Our experiments also indicate that using syllabic rather than character-based tokenization results in better speech recognition performance for Kinyarwanda.

翻译：尽管近期已有大量带标注的基尼亚卢旺达语语音数据可用，但实现鲁棒的语音识别仍具挑战性。本研究表明，采用自监督预训练、微调阶段遵循简单课程学习策略，以及利用半监督学习处理大规模无标签语音数据，能显著提升基尼亚卢旺达语的语音识别性能。我们的方法仅使用公共领域数据：从公开网站采集了一个新的录音室质量语音数据集，并以此训练干净的基线模型；随后利用该基线模型对来自更具多样性和噪声的公共数据集中的样本进行排序，定义简单的课程训练计划；最后通过半监督学习对大规模无标签数据进行标注和迭代学习（共五轮）。最终模型在新数据集上达到3.2%的词错误率（WER），在Mozilla Common Voice基准上达到15.6%的WER，据我们所知为当前最优。实验还表明，对于基尼亚卢旺达语，采用音节而非字符级分词可取得更优的语音识别性能。