Cascaded Cross-Module Residual Learning Towards Lightweight End-to-End Speech Coding

Kai Zhen, Jongmo Sung, Mi Suk Lee, Seungkwon Beack, Minje Kim


Speech codecs learn compact representations of speech signals to facilitate data transmission. Many recent deep neural network (DNN) based end-to-end speech codecs achieve low bitrates and high perceptual quality at the cost of model complexity. We propose a cross-module residual learning (CMRL) pipeline as a module carrier with each module reconstructing the residual from its preceding modules. CMRL differs from other DNN-based speech codecs, in that rather than modeling speech compression problem in a single large neural network, it optimizes a series of less-complicated modules in a two-phase training scheme. The proposed method shows better objective performance than AMR-WB and the state-of-the-art DNN-based speech codec with a similar network architecture. As an end-to-end model, it takes raw PCM signals as an input, but is also compatible with linear predictive coding (LPC), showing better subjective quality at high bitrates than AMR-WB and OPUS. The gain is achieved by using only 0.9 million trainable parameters, a significantly less complex architecture than the other DNN-based codecs in the literature.


 DOI: 10.21437/Interspeech.2019-1816

Cite as: Zhen, K., Sung, J., Lee, M.S., Beack, S., Kim, M. (2019) Cascaded Cross-Module Residual Learning Towards Lightweight End-to-End Speech Coding. Proc. Interspeech 2019, 3396-3400, DOI: 10.21437/Interspeech.2019-1816.


@inproceedings{Zhen2019,
  author={Kai Zhen and Jongmo Sung and Mi Suk Lee and Seungkwon Beack and Minje Kim},
  title={{Cascaded Cross-Module Residual Learning Towards Lightweight End-to-End Speech Coding}},
  year=2019,
  booktitle={Proc. Interspeech 2019},
  pages={3396--3400},
  doi={10.21437/Interspeech.2019-1816},
  url={http://dx.doi.org/10.21437/Interspeech.2019-1816}
}