Enhanced audio-visual speech enhancement with posterior sampling methods in recurrent variational autoencoders
Why this work is in the frame
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Bibliographic record
Abstract
Recovering intelligible speech in noise is essential for robust communication. This work presents an audio-visual speech enhancement framework based on a Recurrent Variational Autoencoder (AV-RVAE), where posterior inference is extended using sampling-based methods including the Metropolis-Adjusted Langevin Algorithm (MALA), Langevin Dynamics EM (LDEM), Hamiltonian Monte Carlo (HMC), Barker sampling, and a hybrid MALA+Barker variant. To isolate the contribution of visual cues, an audio-only baseline (A-RVAE) is trained and evaluated under identical data and inference conditions. Performance is assessed using Scale-Invariant Signal-to-Distortion Ratio (SI-SDR), Perceptual Evaluation of Speech Quality (PESQ), and Short-Time Objective Intelligibility (STOI), along with anytime convergence curves (metric versus wall-clock time) and the Real-Time Factor (RTF; ratio of runtime to audio duration) to measure computational efficiency. Experimental results show that the hybrid MALA+Barker sampler achieves the best overall performance, while LDEM and step-size-optimized MALA exhibit the lowest RTFs, the MALA+Barker sampler offers the most favorable balance between efficiency and enhancement quality. Across all sampling strategies, the AV-RVAE consistently surpasses the audio-only baseline, particularly at low SNRs, confirming the benefit of visual fusion combined with advanced posterior sampling for robust speech enhancement in challenging acoustic environments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it