Training objectives based on predictive coding have recently been shown to be
very effective at learning meaningful representations from unlabeled speech.
One example is Autoregressive Predictive Coding (Chung et al., 2019), which
trains an autoregressive RNN to generate an unseen future frame given a context
such as recent past frames. The basic hypothesis of these approaches is that
hidden states that can accurately predict future frames are a useful
representation for many downstream tasks. In this paper we extend this
hypothesis and aim to enrich the information encoded in the hidden states by
training the model to make more accurate future predictions. We propose an
auxiliary objective that serves as a regularization to improve generalization
of the future frame prediction task. Experimental results on phonetic
classification, speech recognition, and speech translation not only support the
hypothesis, but also demonstrate the effectiveness of our approach in learning
representations that contain richer phonetic content.

通过未标记的语音学习有意义的表示方法，将预测编码的培训目标应用于自回归预测编码（ARPC），并提出了一种辅助目标以提高未来帧预测任务的泛化能力和语音内容。不仅支持假设，还证明了该方法在学习含有更丰富语音内容的表示方面的有效性。