Perceptual Differences and Preferences in Speech Enhancement

Simon Dahl Jepsen; Jesper Rindom Jensen

Home/Archive/arXiv

arXiv · 2610.36892

Perceptual Differences and Preferences in Speech Enhancement

Simon Dahl Jepsen·Jesper Rindom Jensen

View original source ↗Read source document ↗Export metadata ↓

Abstract

Speech enhancement (SE) systems are commonly evaluated with objective metrics, with and without references, and with categorical subjective listening tests, all measuring speech quality and intelligibility. So far, it is unclear from such evaluations whether the processed speech is perceptually different or is preferred by everyday listeners. We investigate discrimination and preference as complementary dimensions of SE evaluation through crowdsourced triangle and pairwise preference tests across four SE systems, with comparisons to clean references. Results show that both listeners' ability to distinguish and whether they exhibit systematic preference vary across systems and substantially across acoustic conditions. Pairwise evaluations of objective metrics showed variable correlation with both discriminability and preference, motivating further studies into these novel aspects in speech enhancement evaluation.

Explore related subjects

eess.AS cs.SD

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Simon Dahl Jepsen, Jesper Rindom Jensen. 2026-10-27. Perceptual Differences and Preferences in Speech Enhancement. https://arxiv.org/abs/2610.36892

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Integrated Spoofing-Robust Automatic Speaker Verification via a Three-Class Formulation and LLR

Spoofing-robust automatic speaker verification (SASV) aims to integrate automatic speaker verification (ASV) and countermeasure (CM). A popular solution is fusion of independent ASV and CM scores. To better modeling SASV, some frameworks integrate ASV and CM within a single network. However, these solutions are typically bi-encoder based, offer limited interpretability, and cannot be readily adapted to new evaluation parameters without retraining. Based on this, we propose a unified end-to-end framework via a three-class formulation that enables log-likelihood ratio (LLR) inference from class logits for a more interpretable decision pipeline. Experiments show comparable performance to existing methods on ASVSpoof5 and better results on SpoofCeleb. The visualization and analysis also prove that the three-class reformulation provides more interpretability.

eess.AS↗

arXiv2026

A Lightweight Dual-Branch Visual Front-End for Efficient Audio-Visual Target Speaker Extraction

Audio-Visual Target Speaker Extraction (AVTSE) uses face-related visual cues to extract a target speaker from a speech mixture. In many AVTSE systems, visual features are obtained from convolutional image encoders, and their computation may become a non-negligible part of the overall system when lightweight separation backbones are used. This work proposes a dual-branch visual front-end that reorganizes visual information according to its spatial characteristics. A compact lip region of interest is used to model local articulation cues through convolutional processing, while facial landmark coordinates are used to represent global geometric motion with a lightweight temporal convolutional network. The two representations are integrated into IIANet and SpatialNet with limited fusion overhead. Experiments on LRS3-2mix under a unified reduced training protocol show that the proposed method achieves a performance-computation trade-off close to the lightweight image-based baseline. Compared with the ResNet18-based visual encoder, it reduces the module-level visual encoding computation by more than 90% across both evaluated separation backbones, with a moderate performance gap. This comparison excludes face detection and landmark estimation and therefore applies to systems where landmark coordinates are already available from preprocessing. Demos are available at https://anonymous.4open.science/r/Lightweight-Visual-Frontend-E46E.

eess.AS↗

arXiv2026

StreamSE: Neural Audio Codec-based Streaming Speech Enhancement for On-device Inference

Recent generative speech enhancement (SE) models achieve high perceptual quality, but their non-causal processing and expensive computation make them difficult to use for streaming, on-device inference. We propose StreamSE, a 20M-parameter streaming SE model that operates in the latent space of a frozen pretrained neural audio codec. At each frame, StreamSE directly regresses a clean continuous latent from the noisy continuous latent while conditioning on its own predictions from previous frames. Enhancement therefore requires a single forward pass per frame and no iterative sampling or look-ahead. We further truncate the codec's residual vector quantizer (RVQ) to its coarse levels, halving the number of tokens per frame while improving robustness to reverberation. StreamSE is the only streaming generative model in our comparison, yet it matches or exceeds the perceptual quality of the offline generative baselines. Furthermore, the complete StreamSE pipeline runs in real time on a single smartphone CPU core. Our results show that continuous latent regression provides a practical route to generative SE under fully causal, on-device streaming constraints.

eess.AS↗