# Full-Reference Speech Quality Estimation with Attentional Siamese Neural Networks

arXiv: https://arxiv.org/abs/2105.00783 · HTML: https://mlsanity.com/paper/2105.00783/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model based on BLSTM](https://mlsanity.com/paper/1808.05344/) (2018)
- [NeuralWarp: Time-Series Similarity with Warping Networks](https://mlsanity.com/paper/1812.08306/) (2018)
- [Non-intrusive speech quality assessment using neural networks](https://mlsanity.com/paper/1903.06908/) (2019)
- [WEnets: A Convolutional Framework for Evaluating Audio Waveforms](https://mlsanity.com/paper/1909.09024/) (2019)

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

- [PhysiQ: Off-site Quality Assessment of Exercise in Physical Therapy](https://mlsanity.com/paper/2211.08245/) (2022)

## Abstract (arXiv)

In this paper, we present a full-reference speech quality prediction model with a deep learning approach. The model determines a feature representation of the reference and the degraded signal through a siamese recurrent convolutional network that shares the weights for both signals as input. The resulting features are then used to align the signals with an attention mechanism and are finally combined to estimate the overall speech quality. The proposed network architecture represents a simple solution for the time-alignment problem that occurs for speech signals transmitted through Voice-Over-IP networks and shows how the clean reference signal can be incorporated into speech quality models that are based on end-to-end trained neural networks.