Overview Of The 2023 Icassp Sp Clarity Challenge: Speech Enhancement For Hearing Aids
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Abstract (arXiv)
This paper reports on the design and outcomes of the ICASSP SP Clarity Challenge: Speech Enhancement for Hearing Aids. The scenario was a listener attending to a target speaker in a noisy, domestic environment. There were multiple interferers and head rotation by the listener. The challenge extended the second Clarity Enhancement Challenge (CEC2) by fixing the amplification stage of the hearing aid; evaluating with a combined metric for speech intelligibility and quality; and providing two evaluation sets, one based on simulation and the other on real-room measurements. Five teams improved on the baseline system for the simulated evaluation set, but the performance on the measured evaluation set was much poorer. Investigations are on-going to determine the exact cause of the mismatch between the simulated and measured data sets. The presence of transducer noise in the measurements, lower order Ambisonics harming the ability for systems to exploit binaural cues and the differences between real and simulated room impulse responses are suggested causes
Key facts. Cited by 6 papers in the map (data through 19 Oct 2026). First posted on arXiv 24 Nov 2023.
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Citing papers per year: 2024: 3; 2025: 2; 2026: 1.
6 papers.
- 2025AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancementcited by 35
- 2024SuperM2M: Supervised and Mixture-to-Mixture Co-Learning for Speech Enhancement and Noise-Robust ASRcited by 6
- 2024Ultra-Low Latency Speech Enhancement - A Comprehensive Studycited by 3
- 2024ctPuLSE: Close-Talk, and Pseudo-Label Based Far-Field, Speech Enhancementcited by 2
- 2025Mixture to Beamformed Mixture: Leveraging Beamformed Mixture as Weak-Supervision for Speech Enhancement and Noise-Robust ASRcited by 0
- 2026A Probabilistic Generative Model for Spectral Speech Enhancementcited by 0
- 2026A Probabilistic Generative Model for Spectral Speech Enhancement30 Mar 2026
- 2025Mixture to Beamformed Mixture: Leveraging Beamformed Mixture as Weak-Supervision for Speech Enhancement and Noise-Robust ASR21 Jul 2025
- 2025AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement26 Jan 2025
- 2024Ultra-Low Latency Speech Enhancement - A Comprehensive Study16 Sep 2024
- 2024ctPuLSE: Close-Talk, and Pseudo-Label Based Far-Field, Speech Enhancement28 Jul 2024
- 2024SuperM2M: Supervised and Mixture-to-Mixture Co-Learning for Speech Enhancement and Noise-Robust ASR15 Mar 2024
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- 2022ICASSP 2022 Acoustic Echo Cancellation Challenge
- 2026ICASSP 2026 URGENT Speech Enhancement Challenge
- 2023ICASSP 2023 Acoustic Echo Cancellation Challenge
- 2023ICASSP 2023 Deep Noise Suppression Challenge
- 2026Active noise cancellation extends the benefit of AI speech enhancement in real hearing-aid recordings ↗
- 2024Real-time multichannel deep speech enhancement in hearing aids: Comparing monaural and binaural processing in complex acoustic scenarios
- 2024Overview of the L3DAS23 Challenge on Audio-Visual Extended Reality
- 2023Multi-Channel Target Speaker Extraction with Refinement: The WavLab Submission to the Second Clarity Enhancement Challenge