# Influence of Clean Speech Characteristics on Speech Enhancement Performance

arXiv: https://arxiv.org/abs/2509.18885 · HTML: https://mlsanity.com/paper/2509.18885/

## Key facts

- Cited by 2 papers in the map (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Placed in

- Speech and audio language models / [Speech enhancement and noise suppression](https://mlsanity.com/speech/speech-enhancement-and-noise-suppression/)

## Builds on (most cited first, 18 of 18)

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Speech Enhancement and Dereverberation with Diffusion-based Generative Models](https://mlsanity.com/paper/2208.05830/) (2022)
- [Conditional Diffusion Probabilistic Model for Speech Enhancement](https://mlsanity.com/paper/2202.05256/) (2022)
- [StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation](https://mlsanity.com/paper/2212.11851/) (2022)
- [A Study on Speech Enhancement Based on Diffusion Probabilistic Model](https://mlsanity.com/paper/2107.11876/) (2021)
- [Improving noise robust automatic speech recognition with single-channel time-domain enhancement network](https://mlsanity.com/paper/2003.03998/) (2020)
- [Schr"odinger Bridge for Generative Speech Enhancement](https://mlsanity.com/paper/2407.16074/) (2024)
- [Schrodinger Bridges Beat Diffusion Models on Text-to-Speech Synthesis](https://mlsanity.com/paper/2312.03491/) (2023)
- [Personalized Speech Enhancement: New Models and Comprehensive Evaluation](https://mlsanity.com/paper/2110.09625/) (2021)
- [Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation](https://mlsanity.com/paper/2105.03544/) (2021)
- [Assessing the Generalization Gap of Learning-Based Speech Enhancement Systems in Noisy and Reverberant Environments](https://mlsanity.com/paper/2309.06183/) (2023)
- [Lessons Learned from the URGENT 2024 Speech Enhancement Challenge](https://mlsanity.com/paper/2506.01611/) (2025)
- [Personalized Speech Enhancement Without a Separate Speaker Embedding Model](https://mlsanity.com/paper/2406.09928/) (2024)
- [Deep-Learning-Based Audio-Visual Speech Enhancement in Presence of Lombard Effect](https://mlsanity.com/paper/1905.12605/) (2019)
- [Variational Autoencoder for Personalized Pathological Speech Enhancement](https://mlsanity.com/paper/2503.14036/) (2025)
- [Navigating PESQ: Up-to-Date Versions and Open Implementations](https://mlsanity.com/paper/2505.19760/) (2025)

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

- [Data Augmentation for Pathological Speech Enhancement](https://mlsanity.com/paper/2602.14671/) (2026)
- [Rethinking Batch Composition: Promoting Feature Diversity for Speech Enhancement](https://mlsanity.com/paper/2610.21803/) (2026)

## Abstract (arXiv)

Speech enhancement (SE) performance is known to depend on noise characteristics and signal to noise ratio (SNR), yet intrinsic properties of the clean speech signal itself remain an underexplored factor. In this work, we systematically analyze how clean speech characteristics influence enhancement difficulty across multiple state of the art SE models, languages, and noise conditions. We extract a set of pitch, formant, loudness, and spectral flux features from clean speech and compute correlations with objective SE metrics, including frequency weighted segmental SNR and PESQ. Our results show that formant amplitudes are consistently predictive of SE performance, with higher and more stable formants leading to larger enhancement gains. We further demonstrate that performance varies substantially even within a single speaker's utterances, highlighting the importance of intraspeaker acoustic variability. These findings provide new insights into SE challenges, suggesting that intrinsic speech characteristics should be considered when designing datasets, evaluation protocols, and enhancement models.