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 four successive generations.
Our final model achieves 3.2% word error rate (WER) on the new dataset and
15.9% 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.

通过自监督预训练、课程表安排进行微调以及利用大规模未标记语音数据的半监督学习，我们展示出在金雅琳达语（Kinyarwanda）的语音识别性能方面的显著提升。我们的方法仅关注使用公共领域数据，在公共网站上收集了一个新的高质量语音数据集，然后用于训练一个干净的基线模型，该模型再用于对来自多样且噪声较大的公共数据集中的示例进行排名，从而定义了一个简单的课程表训练计划。最后，我们采用四代连续的半监督学习方法对大规模未标记数据进行标记和学习。根据我们所知，我们的最终模型在新数据集上的字错率（WER）为 3.2％，在 Mozilla Common Voice 基准上的 WER 为 15.9％，这是目前最先进的。我们的实验还表明，对于金雅琳达语的语音识别性能而言，采用音节而不是字符的分词方法结果更好。