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.

翻译：尽管近期已获得大量标注的基尼亚卢旺达语语音数据，但实现该语言的稳健语音识别仍具挑战性。本研究证明，采用自监督预训练、在微调阶段遵循简易课程调度策略，并利用半监督学习来挖掘大量无标注语音数据，能显著提升基尼亚卢旺达语的语音识别性能。我们的方法仅依赖公共领域数据：首先从公开网站收集一套全新录音室级语音数据集，并以此训练干净基线模型；随后利用该模型对更具多样性与噪声的公开数据集样本进行排序，定义简易课程训练调度方案；最后通过四代迭代式半监督学习，对大规模无标注数据进行标注与训练。最终模型在新数据集上实现了3.2%的词错误率（WER），在Mozilla Common Voice基准上达到15.9%的WER——据我们所知，此为当前最优性能。实验还表明，对基尼亚卢旺达语而言，采用音节级而非字符级分词能获得更优的语音识别效果。