# MOSRA: Joint Mean Opinion Score and Room Acoustics Speech Quality Assessment

arXiv: https://arxiv.org/abs/2204.01345 · HTML: https://mlsanity.com/paper/2204.01345/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets](https://mlsanity.com/paper/2104.09494/) (2021)
- [Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model based on BLSTM](https://mlsanity.com/paper/1808.05344/) (2018)
- [Non-intrusive speech quality assessment using neural networks](https://mlsanity.com/paper/1903.06908/) (2019)
- [MetricNet: Towards Improved Modeling For Non-Intrusive Speech Quality Assessment](https://mlsanity.com/paper/2104.01227/) (2021)
- [DNN No-Reference PSTN Speech Quality Prediction](https://mlsanity.com/paper/2007.14598/) (2020)
- [A Universal Deep Room Acoustics Estimator](https://mlsanity.com/paper/2109.14436/) (2021)
- [Joint Blind Room Acoustic Characterization From Speech And Music Signals Using Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/2010.11167/) (2020)

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

- [Efficient Speech Quality Assessment using Self-supervised Framewise Embeddings](https://mlsanity.com/paper/2211.06646/) (2022)
- [ALO-VC: Any-to-any Low-latency One-shot Voice Conversion](https://mlsanity.com/paper/2306.01100/) (2023)

## Abstract (arXiv)

The acoustic environment can degrade speech quality during communication (e.g., video call, remote presentation, outside voice recording), and its impact is often unknown. Objective metrics for speech quality have proven challenging to develop given the multi-dimensionality of factors that affect speech quality and the difficulty of collecting labeled data. Hypothesizing the impact of acoustics on speech quality, this paper presents MOSRA: a non-intrusive multi-dimensional speech quality metric that can predict room acoustics parameters (SNR, STI, T60, DRR, and C50) alongside the overall mean opinion score (MOS) for speech quality. By explicitly optimizing the model to learn these room acoustics parameters, we can extract more informative features and improve the generalization for the MOS task when the training data is limited. Furthermore, we also show that this joint training method enhances the blind estimation of room acoustics, improving the performance of current state-of-the-art models. An additional side-effect of this joint prediction is the improvement in the explainability of the predictions, which is a valuable feature for many applications.