Enhanced audio-visual speech enhancement with posterior sampling methods in recurrent variational autoencoders

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.