Synthetic realism in speech enhancement: Training models for the real world with ai-coustics

author: Tim Janke, Co-founder and Head of Research at ai-coustics

Voice AI often fails in simple, frustrating ways. A voice agent responds to a TV in the background, transcribes a distant colleague instead of the user, or stalls because it never detects the right speaker. These aren’t rare edge cases, they’re what happens when speech systems leave controlled environments and enter the real world. At ai-coustics, we build the reliability layer for voice AI, ensuring systems behave predictably when conditions are anything but ideal.

Models like Quail Voice Focus are built for exactly this challenge. Automatic speech recognition systems do what they are trained to do: transcribe all audible speech. In many contexts, that’s correct. In interactive voice applications, however, it breaks conversational flow. When background speakers or distant voices are treated as equally important as the user standing next to the device, transcription accuracy drops, turn-taking fails, and voice agents become unreliable or unresponsive.

Solving this problem requires more than architectural tweaks. At ai-coustics, we design lightweight, real-time models and deploy them in our own optimized inference engine but experience has shown that the biggest lever for model quality is data. Behind every great model is an even greater dataset. Teaching a system which voice matters in a given situation requires training data that reflects real acoustic relationships: distance, room acoustics, device placement, and interference. This belief underpins our training philosophy, which we call synthetic realism.

Why synthetic realism matters

Synthetic realism starts with high-quality speech and deliberately degrades it in controlled ways to mirror real usage conditions. We simulate noise, distance, device characteristics, and room acoustics so that training data closely matches what models encounter in production. This allows us to design datasets around concrete behaviors such as handling far-field speech, overlapping speakers, or reverberant environments rather than relying on generic augmentation.

The result is data that doesn’t just sound realistic, but drives reliable behavior. Models trained this way preserve acoustic cues needed for downstream tasks like speech recognition, generalize across devices and environments, and avoid common failure modes such as reacting to background speech. Synthetic realism ensures models learn the structure of real-world audio, not just its surface characteristics.

Bringing physics into the training loop with Treble

To model room acoustics and spatial relationships accurately, we rely on physics-based acoustic simulation. As part of our data generation pipeline, we use the Treble SDK to generate room impulse responses that capture not only room size, but also room shape, materials, and acoustic properties. This enables realistic simulation of how speech and noise interact with physical spaces, beyond what simplified reverberation models can provide.

Crucially, the Treble SDK allows us to place speakers and noise sources at explicit positions relative to the microphone. This spatial control is essential for many training scenarios like Quail Voice Focus where the distinction between foreground and background speakers often depends on distance and geometry. By generating acoustically and spatially structured scenes at scale, we can train and evaluate models on data that reflects how audio is actually captured in real environments.

From realistic data to reliable behavior

Training on acoustically realistic data from the Treble SDK data leads to measurable improvements in real-world performance. Models trained with spatially grounded data generalize more consistently across rooms, devices, and usage scenarios, rather than overfitting to idealized conditions. In our evaluations, this translates into improved word error rates in noisy and reverberant environments and more stable behavior in multi-speaker situations where traditional enhancement approaches often struggle.

For voice-agent applications, the impact is immediate. Models like Quail Voice Focus reduce false activations and missed turns by reliably prioritizing the speaker closest to the device, even in the presence of background speech or media playback. More broadly, the same data-driven approach improves robustness across the Quail model family, ensuring enhancements don’t just sound better in isolation, but meaningfully improve downstream system performance in production.

Closing: Building for production reality

Reliable voice AI is built by training systems on data that reflects how audio behaves in production - chaotic, overlapping, device-dependent, and rarely clean. At ai-coustics, synthetic realism is how we prepare models for this reality, combining lightweight, deployable models with training data that captures real acoustic complexity at scale.

Join the Treble webinar on February 26th, where ai-coustics will join Treble as a guest speaker to show how this training philosophy translates into production systems, measurable performance gains, and what’s next for building reliable voice AI.