# Improving End-to-End Speech Recognition with Policy Learning

arXiv: https://arxiv.org/abs/1712.07101 · HTML: https://mlsanity.com/paper/1712.07101/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2017.

## Built on by (most cited first, 5 of 5)

- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [A Survey on Deep Reinforcement Learning for Audio-Based Applications](https://mlsanity.com/paper/2101.00240/) (2021)
- [Goal-driven text descriptions for images](https://mlsanity.com/paper/2108.12575/) (2021)
- [Improved Contextual Recognition In Automatic Speech Recognition Systems By Semantic Lattice Rescoring](https://mlsanity.com/paper/2310.09680/) (2023)
- [Improving Speech Recognition on Noisy Speech via Speech Enhancement with Multi-Discriminators CycleGAN](https://mlsanity.com/paper/2112.06309/) (2021)

## Abstract (arXiv)

Connectionist temporal classification (CTC) is widely used for maximum likelihood learning in end-to-end speech recognition models. However, there is usually a disparity between the negative maximum likelihood and the performance metric used in speech recognition, e.g., word error rate (WER). This results in a mismatch between the objective function and metric during training. We show that the above problem can be mitigated by jointly training with maximum likelihood and policy gradient. In particular, with policy learning we are able to directly optimize on the (otherwise non-differentiable) performance metric. We show that joint training improves relative performance by 4% to 13% for our end-to-end model as compared to the same model learned through maximum likelihood. The model achieves 5.53% WER on Wall Street Journal dataset, and 5.42% and 14.70% on Librispeech test-clean and test-other set, respectively.