ISCA Archive Interspeech 2022
ISCA Archive Interspeech 2022

Content-Context Factorized Representations for Automated Speech Recognition

David Chan, Shalini Ghosh

Deep neural networks have largely demonstrated their ability to perform automated speech recognition (ASR) by extracting meaningful features from input audio frames. Such features, however, may consist not only of information about the spoken language content, but also may contain information about unnecessary contexts such as background noise and sounds or speaker identity, accent, or protected attributes. Such information can directly harm generalization performance, by introducing spurious correlations between the spoken words and the context in which such words were spoken. In this work, we introduce an unsupervised, encoder-agnostic method for factoring speech-encoder representations into explicit content-encoding representations and spurious context-encoding representations. By doing so, we demonstrate improved performance on standard ASR benchmarks, as well as improved performance in both real-world and artificially noisy ASR scenarios.


doi: 10.21437/Interspeech.2022-390

Cite as: Chan, D., Ghosh, S. (2022) Content-Context Factorized Representations for Automated Speech Recognition. Proc. Interspeech 2022, 61-65, doi: 10.21437/Interspeech.2022-390

@inproceedings{chan22_interspeech,
  author={David Chan and Shalini Ghosh},
  title={{Content-Context Factorized Representations for Automated Speech Recognition}},
  year=2022,
  booktitle={Proc. Interspeech 2022},
  pages={61--65},
  doi={10.21437/Interspeech.2022-390},
  issn={2958-1796}
}