We present a number of low-resource approaches to the tasks of the Zero Resource Speech Challenge 2021. We build on the unsupervised representations of speech proposed by the organizers as a baseline, derived from CPC and clustered with the k-means algorithm. We demonstrate that simple methods of refining those representations can narrow the gap, or even improve upon the solutions which use a high computational budget. The results lead to the conclusion that the CPC-derived representations are still too noisy for training language models, but stable enough for simpler forms of pattern matching and retrieval.

翻译：我们对2021年“零资源演讲挑战”的任务提出了一些低资源方法。我们以组织者作为基线提出的未经监督的演讲陈述为基础,这些陈述来自产品总分类,与k means算法相结合。我们证明,改进这些陈述的简单方法可以缩小差距,甚至改进使用高计算预算的解决方案。其结果是,产品总分类派代表对于培训语言模式来说仍然过于吵闹,但对于更简单的模式匹配和检索形式来说足够稳定。