Abstract
Unsupervised speech recognition is a task of training a speech recognition model with unpaired data. To determine when and how unsupervised speech recognition can succeed, and how classification error relates to candidate training objectives, we develop a theoretical framework for unsupervised speech recognition grounded in classification error bounds. We introduce two conditions under which unsupervised speech recognition is possible. The necessity of these conditions are also discussed. Under these conditions, we derive a classification error bound for unsupervised speech recognition and validate this bound in simulations. Motivated by this bound, we propose a single-stage sequence-level cross-entropy loss for unsupervised speech recognition.
Abstract (translated)
无监督语音识别是指使用未配对数据训练语音识别模型的任务。为了确定何时以及如何能够成功进行无监督语音识别，以及分类误差与候选训练目标之间的关系，我们基于分类误差界限建立了一个理论框架来研究无监督语音识别。我们引入了两个在特定情况下可行的条件，并探讨了这些条件的必要性。在此基础上，我们在这些条件下推导出了无监督语音识别的分类误差界，并通过模拟验证了这一边界。受到这个界的启发，我们提出了一种用于无监督语音识别的一阶段序列级交叉熵损失方法。
URL
https://arxiv.org/abs/2603.02285
