SEQ-CPC : Sequential Contrastive Predictive Coding for Automatic Speech Recognition

Yulong Chen; Jianping Zhao; Weiqi Wang; Ming Fang; Haimei Kang; Lu Wang; Tao Wei; Jun Ma

ICASSP 2021accepted0 citations

SEQ-CPC : Sequential Contrastive Predictive Coding for Automatic Speech Recognition

Yulong Chen, Jianping Zhao, Weiqi Wang, Ming Fang, Haimei Kang, Lu Wang, Tao Wei, Jun Ma

Abstract

Inspired by the contrastive predictive coding (CPC), we propose a feature representation scheme for automatic speech recognition (ASR), which encodes sequential dependency information from raw audio signals. Following the original CPC, for a given frame, mutual information (MI) lower bound is maximized between historical context and future prediction. While computing the MI lower bound, based on original CPC, we develop the sequential CPC (SEQ-CPC), which takes the sequential information between frames into consideration. Since speech frames are not independent events, incorporating sequential information leads to better recognition performance. Experimental results on WSJ corpus show that SEQ-CPC achieves the best performance than CPC and NCE which is the contrastive objective used in wav2vec.

BibTeX

@inproceedings{icassp2021_seqcpcsequential,
  title = {SEQ-CPC : Sequential Contrastive Predictive Coding for Automatic Speech Recognition},
  author = {Yulong Chen and Jianping Zhao and Weiqi Wang and Ming Fang and Haimei Kang and Lu Wang and Tao Wei and Jun Ma and Shaojun Wang and Jing Xiao},
  booktitle = {ICASSP 2021},
  year = {2021}
}