ctPuLSE: Bridging Close-Talk and Pseudo-Label Far-Field Speech Enhancement

ctPuLSE: Bridging Close-Talk and Pseudo-Label Far-Field Speech Enhancement

As far-field devices become pervasive—from smart speakers to conference systems—the gap between the pristine quality of close-talking recordings and the challenges of reverberant, noisy rooms widens. ctPuLSE, short for Close-Talk Pseudo-Label Speech Enhancement, proposes a practical bridge: use high-quality close-talk data to generate pseudo-labels for far-field scenarios, enabling robust enhancement without requiring exhaustive paired far-field datasets. The idea is to transfer the best of both worlds—a clean, close-talking reference and a scalable far-field target—through a carefully designed pseudo-labeling workflow.

What ctPuLSE means

ctPuLSE stands for Close-Talk Pseudo-Label Speech Enhancement. It embodies a two-stage philosophy: first, learn a powerful enhancer from close-talk signals where the target is well-defined; then, leverage those learned targets to train a far-field model using pseudo-labels that approximate the clean speech we would have in an ideal far-field capture. The approach treats far-field outputs as a learning problem guided by the high-quality signals available in close-talk data, mitigating the scarcity of paired far-field clean references.

The challenges ctPuLSE aims to address

• Reverberation and noise in real rooms distort intelligibility and speaker identity.
• Limited availability of paired far-field data with clean references.
• Domain mismatch between close-talk training and far-field deployment.
• Balancing perceptual quality with objective metrics, especially under diverse microphone configurations.

Core ideas behind ctPuLSE

• Leverage high-quality close-talk data: Train a baseline enhancer on clean close-talk pairs to establish a strong reference for speech quality and intelligibility.
• Generate meaningful pseudo-labels for far-field: Use the close-talk-trained model (or a teacher model) to produce targets for far-field inputs, creating a supervisory signal where real clean references are scarce.
• Bridge domains with a staged learning objective: Align far-field inputs to pseudo-label targets in a way that preserves speaker characteristics while suppressing reverberation and noise.
• Iterative refinement and robustness: Optionally iterate the cycle with renewed pseudo-labels and diversified data to reduce label noise and improve generalization.

Architecture and workflow

The ctPuLSE workflow envisions a two-stage training pipeline with optional iterations:

1. Train a high-performance speech enhancer using close-talk paired data. This model focuses on achieving clean spectral magnitudes and temporal structure, serving as the teacher for far-field targets.
2. Apply the Stage 1 model (or an ensemble) to far-field recordings to produce pseudo-clean targets. These targets emulate what the far-field speech would look like if we could capture it with a direct, noise-free signal.
3. Train a far-field enhancer using real far-field inputs paired with the pseudo-labels as supervision. The aim is for the student to generalize the close-talk improvements to distant microphones, phone-calls, and reverberant rooms.
4. Recompute pseudo-labels with the updated model, expand the training set with more diverse acoustics, and repeat to tighten the alignment between domains.

In practice, this flow can be implemented with teacher-student distillation, multi-task learning that includes a reconstruction and a perceptual loss, or a semi-supervised framework that blends real far-field data with pseudo-labeled examples.

Training strategies

• Curriculum learning: Start with mild reverberation and gradually introduce harder acoustics, allowing the far-field model to inherit stable patterns from the close-talk foundation.
• Data augmentation: Simulate diverse rooms, microphone arrays, and noise profiles to improve robustness before applying pseudo-labels.
• Noise-aware objectives: Incorporate loss terms that balance dereverberation with denoising to preserve speech naturalness and intelligibility.
• Domain adaptation measures: Use feature normalization, adaptive front-ends, or conditional modules that tailor processing to specific microphone configurations.

Evaluation and expected gains

Success for ctPuLSE hinges on both objective metrics and real-world perception. Key targets include:

• PESQ improvements over baselines, especially in mid-to-high frequency bands affected by reflections.
• STOI gains indicating better intelligibility, particularly in challenging SNR regimes.
• SI-SDR improvements reflecting cleaner separation of speech from reverberation and noise.
• Subjective listening tests that confirm naturalness and speaker similarity are preserved across devices.

“A strong close-talk base paired with thoughtful pseudo-labeling can unlock far-field performance that previously required impractical data collection.”

Limitations and future directions

• pseudo-label quality depends on the fidelity of the close-talk model; errors can propagate if not carefully managed.
• Label noise may necessitate noise-robust loss functions and selective fine-tuning.
• Computational cost grows with iterative refinement and multi-condition training; practical deployment must consider latency constraints.

Takeaways

ctPuLSE offers a pragmatic pathway to enhance far-field speech by marrying the precision of close-talk training with the scalability of pseudo-label supervision. By thoughtfully coordinating stages, losses, and data diversity, this approach aspires to deliver clearer, more natural speech across devices and environments—without demanding prohibitively large paired far-field datasets.