Contrastive Predictive Coding (CPC) is a representation learning method that
maximizes the mutual information between intermediate latent representations
and the output of a given model. It can be used to effectively initialize the
encoder of an Automatic Speech Recognition (ASR) model. We present a novel
modification of CPC called Guided Contrastive Predictive Coding (GCPC). Our
proposed method maximizes the mutual information between representations from a
prior-knowledge model and the output of the model being pre-trained, allowing
prior knowledge injection during pre-training. We validate our method on 3 ASR
tasks: German, French and English. Our method outperforms CPC pre-training on
all three datasets, reducing the Word Error Rate (WER) by 4.44%, 6.55% and
15.43% relative on the German, French and English (Librispeech) tasks
respectively, compared to training from scratch, while CPC pre-training only
brings 2.96%, 1.01% and 14.39% relative WER reduction respectively.

本研究提出了一种名为 GCPC 的改进型 CPC 方法，可以注入先验知识并在 Tacotron2 上进行预训练，实验表明，相比于 CPC，该方法在德语，法语和英语等自动语音识别任务中均表现出更好的识别效果。