# DNN No-Reference PSTN Speech Quality Prediction

arXiv: https://arxiv.org/abs/2007.14598 · HTML: https://mlsanity.com/paper/2007.14598/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [MOSRA: Joint Mean Opinion Score and Room Acoustics Speech Quality Assessment](https://mlsanity.com/paper/2204.01345/) (2022)
- [Wideband Audio Waveform Evaluation Networks: Efficient, Accurate Estimation of Speech Qualities](https://mlsanity.com/paper/2206.13272/) (2022)
- [Efficient Speech Quality Assessment using Self-supervised Framewise Embeddings](https://mlsanity.com/paper/2211.06646/) (2022)
- [Automatic Speech Recognition System-Independent Word Error Rate Estimation](https://mlsanity.com/paper/2404.16743/) (2024)
- [WhiSQA: Non-Intrusive Speech Quality Prediction Using Whisper Encoder Features](https://mlsanity.com/paper/2508.02210/) (2025)
- [EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events](https://mlsanity.com/paper/2309.01480/) (2023)
- [Residual-Guided Non-Intrusive Speech Quality Assessment](https://mlsanity.com/paper/2203.11499/) (2022)
- [DAMOS: Learning Distortion-Aware Speech Quality Assessment through Explicit Distortion Localization](https://mlsanity.com/paper/2608.21176/) (2026)

## Abstract (arXiv)

Classic public switched telephone networks (PSTN) are often a black box for VoIP network providers, as they have no access to performance indicators, such as delay or packet loss. Only the degraded output speech signal can be used to monitor the speech quality of these networks. However, the current state-of-the-art speech quality models are not reliable enough to be used for live monitoring. One of the reasons for this is that PSTN distortions can be unique depending on the provider and country, which makes it difficult to train a model that generalizes well for different PSTN networks. In this paper, we present a new open-source PSTN speech quality test set with over 1000 crowdsourced real phone calls. Our proposed no-reference model outperforms the full-reference POLQA and no-reference P.563 on the validation and test set. Further, we analyzed the influence of file cropping on the perceived speech quality and the influence of the number of ratings and training size on the model accuracy.