# Sequence Training of DNN Acoustic Models With Natural Gradient

arXiv: https://arxiv.org/abs/1804.02204 · HTML: https://mlsanity.com/paper/1804.02204/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2018.

## Built on by (most cited first, 2 of 2)

- [Asynchronous Decentralized Distributed Training of Acoustic Models](https://mlsanity.com/paper/2110.11199/) (2021)
- [A Distributed Optimisation Framework Combining Natural Gradient with Hessian-Free for Discriminative Sequence Training](https://mlsanity.com/paper/2103.07554/) (2021)

## Abstract (arXiv)

Deep Neural Network (DNN) acoustic models often use discriminative sequence training that optimises an objective function that better approximates the word error rate (WER) than frame-based training. Sequence training is normally implemented using Stochastic Gradient Descent (SGD) or Hessian Free (HF) training. This paper proposes an alternative batch style optimisation framework that employs a Natural Gradient (NG) approach to traverse through the parameter space. By correcting the gradient according to the local curvature of the KL-divergence, the NG optimisation process converges more quickly than HF. Furthermore, the proposed NG approach can be applied to any sequence discriminative training criterion. The efficacy of the NG method is shown using experiments on a Multi-Genre Broadcast (MGB) transcription task that demonstrates both the computational efficiency and the accuracy of the resulting DNN models.