# Non-intrusive speech quality assessment using neural networks

arXiv: https://arxiv.org/abs/1903.06908 · HTML: https://mlsanity.com/paper/1903.06908/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2019.

## Built on by (most cited first, 11 of 11)

- [NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets](https://mlsanity.com/paper/2104.09494/) (2021)
- [Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features](https://mlsanity.com/paper/2111.02363/) (2021)
- [CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement](https://mlsanity.com/paper/2209.11112/) (2022)
- [Utilizing Self-supervised Representations for MOS Prediction](https://mlsanity.com/paper/2104.03017/) (2021)
- [Diffiner: A Versatile Diffusion-based Generative Refiner for Speech Enhancement](https://mlsanity.com/paper/2210.17287/) (2022)
- [MOSRA: Joint Mean Opinion Score and Room Acoustics Speech Quality Assessment](https://mlsanity.com/paper/2204.01345/) (2022)
- [Automatic Evaluation of Speaker Similarity](https://mlsanity.com/paper/2207.00344/) (2022)
- [On the robustness of non-intrusive speech quality model by adversarial examples](https://mlsanity.com/paper/2211.06508/) (2022)
- [Efficient Speech Quality Assessment using Self-supervised Framewise Embeddings](https://mlsanity.com/paper/2211.06646/) (2022)
- [Full-Reference Speech Quality Estimation with Attentional Siamese Neural Networks](https://mlsanity.com/paper/2105.00783/) (2021)
- [Residual-Guided Non-Intrusive Speech Quality Assessment](https://mlsanity.com/paper/2203.11499/) (2022)

## Abstract (arXiv)

Estimating the perceived quality of an audio signal is critical for many multimedia and audio processing systems. Providers strive to offer optimal and reliable services in order to increase the user quality of experience (QoE). In this work, we present an investigation of the applicability of neural networks for non-intrusive audio quality assessment. We propose three neural network-based approaches for mean opinion score (MOS) estimation. We compare our results to three instrumental measures: the perceptual evaluation of speech quality (PESQ), the ITU-T Recommendation P.563, and the speech-to-reverberation energy ratio. Our evaluation uses a speech dataset contaminated with convolutive and additive noise, labeled using a crowd-based QoE evaluation, evaluated with Pearson correlation with MOS labels, and mean-squared-error of the estimated MOS. Our proposed approaches outperform the aforementioned instrumental measures, with a fully connected deep neural network using Mel-frequency features providing the best correlation (0.87) and the lowest mean squared error (0.15)