P.808 Multilingual Speech Enhancement Testing: Approach and Results of URGENT 2025 Challenge
On the map: Speech and audio language models → Speech enhancement and noise suppression
Abstract (arXiv)
In speech quality estimation for speech enhancement (SE) systems, subjective listening tests so far are considered as the gold standard. This should be even more true considering the large influx of new generative or hybrid methods into the field, revealing issues of some objective metrics. Efforts such as the Interspeech 2025 URGENT Speech Enhancement Challenge also involving non-English datasets add the aspect of multilinguality to the testing procedure. In this paper, we provide a brief recap of the ITU-T P.808 crowdsourced subjective listening test method. A first novel contribution is our proposed process of localizing both text and audio components of Naderi and Cutler's implementation of crowdsourced subjective absolute category rating (ACR) listening tests involving text-to-speech (TTS). Further, we provide surprising analyses of and insights into URGENT Challenge results, tackling the reliability of (P.808) ACR subjective testing as gold standard in the age of generative AI. Particularly, it seems that for generative SE methods, subjective (ACR MOS) and objective (DNSMOS, NISQA) reference-free metrics should be accompanied by objective phone fidelity metrics to reliably detect hallucinations. Finally, we will soon release our localization scripts and methods for easy deployment for new multilingual speech enhancement subjective evaluations according to ITU-T P.808.
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 26 of its 49 references that are in the map. First posted on arXiv 15 Jul 2025.
Builds on
26 of 49 references.
Closest ancestors · 6
Same direction, newest first
- 2025Interspeech 2025 URGENT Speech Enhancement Challenge29 May
- 2024Vibravox: A Dataset of French Speech Captured with Body-conduction Audio Sensors16 Jul
- 2024EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation10 Jun
- 2024URGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement7 Jun
- 2023On the Behavior of Intrusive and Non-intrusive Speech Enhancement Metrics in Predictive and Generative Settings5 Jun
- 2022Speech Enhancement and Dereverberation with Diffusion-based Generative Models11 Aug
Other references · 20
- 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,596
- 2019LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speechcited by 864
- 2020MLS: A Large-Scale Multilingual Dataset for Speech Researchcited by 589
- 2022FLEURS: Few-shot Learning Evaluation of Universal Representations of Speechcited by 539
- 2020DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressorscited by 356
- 2021NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasetscited by 270
- 2020CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordingscited by 231
- 2021AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenariocited by 125
- 2023ICASSP 2023 Deep Noise Suppression Challengecited by 101
- 2024YODAS: Youtube-Oriented Dataset for Audio and Speechcited by 79
- 2020An Open source Implementation of ITU-T Recommendation P.808 with Validationcited by 44
- 2024ICASSP 2024 Speech Signal Improvement Challengecited by 30
8 more
- 2023JVNV: A Corpus of Japanese Emotional Speech with Verbal Content and Nonverbal Expressionscited by 14
- 2022NESC: Robust Neural End-2-End Speech Coding with GANscited by 8
- 2024Crowdsourced Multilingual Speech Intelligibility Testingcited by 6
- 2022Disentangling the Impacts of Language and Channel Variability on Speech Separation Networkscited by 5
- 2024Hallucination in Perceptual Metric-Driven Speech Enhancement Networkscited by 5
- 2020Application of Just-Noticeable Difference in Quality as Environment Suitability Test for Crowdsourcing Speech Quality Assessment Taskcited by 3
- 2023Study on the Correlation between Objective Evaluations and Subjective Speech Quality and Intelligibilitycited by 2
- 2025Language Barriers: Evaluating Cross-Lingual Performance of CNN and Transformer Architectures for Speech Quality Estimationcited by 2
Built on by
Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2026The VoiceMOS Challenge 2026: Evaluating Speech Enhancement, Emotional TTS and Accented TTS Systems
- 2026Too Good to Be True: A Study on Modern Automatic Speech Recognition for the Evaluation of Speech Enhancement
- 2023On Crowdsourcing-design with Comparison Category Rating for Evaluating Speech Enhancement Algorithms
- 2023Multi-dimensional Speech Quality Assessment in Crowdsourcing
- 2025The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties
- 2025Uni-VERSA: Versatile Speech Assessment with a Unified Network
- 2026Improving Predicted MOS Scores, Not Perceived Quality: Multi-Predictor Test-Time Optimization of Enhanced Speech
- 2025Lessons Learned from the URGENT 2024 Speech Enhancement Challenge