# Speech Prediction using an Adaptive Recurrent Neural Network with Application to Packet Loss Concealment

arXiv: https://arxiv.org/abs/2111.08116 · HTML: https://mlsanity.com/paper/2111.08116/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2021.

## Built on by (most cited first, 1 of 1)

- [Is Self-Supervised Learning Enough to Fill in the Gap? A Study on Speech Inpainting](https://mlsanity.com/paper/2405.20101/) (2024)

## Abstract (arXiv)

This paper proposes a novel approach for speech signal prediction based on a recurrent neural network (RNN). Unlike existing RNN-based predictors, which operate on parametric features and are trained offline on a large collection of such features, the proposed predictor operates directly on speech samples and is trained online on the recent past of the speech signal. Optionally, the network can be pre-trained offline to speed-up convergence at start-up. The proposed predictor is a single end-to-end network that captures all sorts of dependencies between samples, and therefore has the potential to outperform classical linear/non-linear and short-term/long-term speech predictor structures. We apply it to the packet loss concealment (PLC) problem and show that it outperforms the standard ITU G.711 Appendix I PLC technique.