Automatic speech recognition (ASR) has been established as a well-performing
technique for many scenarios where lots of labeled data is available.
Additionally, unsupervised representation learning recently helped to tackle
tasks with limited data. Following this, hardware limitations and applications
give rise to the question how to efficiently take advantage of large pretrained
models and reduce their complexity for downstream tasks. In this work, we study
a challenging low resource conversational telephony speech corpus from the
medical domain in Vietnamese and German. We show the benefits of using
unsupervised techniques beyond simple fine-tuning of large pre-trained models,
discuss how to adapt them to a practical telephony task including bandwidth
transfer and investigate different data conditions for pre-training and
fine-tuning. We outperform the project baselines by 22% relative using
pretraining techniques. Further gains of 29% can be achieved by refinements of
architecture and training and 6% by adding 0.8 h of in-domain adaptation data.

本研究探讨了在越南语和德语的医疗领域低资源对话电话语音语料库中使用无监督方法进行预训练，并讨论了如何适应实际的电话任务，包括带宽传输和不同的数据条件，该方法超过了基线 22% 的相对性能。