Overview
SB-RF is a proposed generative framework for speech enhancement (SE) designed to operate in a single inference step. This method integrates Rectified Flow (RF) with Schrödinger Bridge (SB) theory. The design aims to address the multi-step inference often associated with generative models for SE, which can restrict low-latency deployment capabilities.
Research Context
Generative models have demonstrated effectiveness in speech enhancement tasks. However, a common characteristic of these models is their reliance on multi-step inference procedures. This multi-step nature can present a limitation when aiming for low-latency deployment in real-world applications where rapid processing is critical.
Approach
The SB-RF framework combines two theoretical components: Rectified Flow (RF) and Schrödinger Bridge (SB) theory. The training process for SB-RF involves sampling intermediate states. These intermediate states are drawn from an SB time marginal. Concurrently, a conditional velocity field is trained using the velocity-matching objective inherent to the RF methodology.
During the inference phase, SB-RF initiates its process from a noisy observation. Subsequently, it employs a single Euler update. This single-step update mechanism is central to its design for enabling low-latency operation.
Findings
- SB-RF achieved competitive performance among generative methods when evaluated on the VoiceBank-DEMAND benchmark.
- Further assessment was conducted on a simulated low signal-to-noise ratio (SNR) test set. This evaluation utilized an expanded training dataset.
- Under these specific conditions (simulated low SNR, expanded training dataset), SB-RF demonstrated superior performance compared to the baselines with which it was compared.
Why This Matters
The superior performance of SB-RF in simulated low signal-to-noise ratio conditions, coupled with its one-step inference capability, suggests its potential for real-world applications where both robustness to noise and low latency are critical factors in speech enhancement systems.