Synthetic data enables complete control over acoustic conditions, perfectly labelled outputs, and rapid generation of scenarios that would be impractical or impossible to record.
Treble for Voice AI
Generate the acoustic data your speech models are missing
Build large-scale training and evaluation datasets around the conditions your model needs to handle. Control conversations and background noise, with clean reference targets and spatial labels to help you understand where performance breaks down.
The Challenge
A good benchmark score does not tell the whole story
A voice agent might understand close-mic speech but struggle with someone speaking across a reverberant kitchen while the television is on. The words have not changed. The audio reaching the model has.
Recording campaigns cover only a fraction of these conditions. Basic augmentation can also miss effects such as sound bending around obstacles or building up at particular frequencies in a room. Those gaps leave teams unsure how their models will behave in unfamiliar environments, especially when people speak over each other.
How Treble Helps
Build the dataset your problem needs
Fill the gaps between the recordings you have and the conditions your model will encounter. Generate targeted data for a specific deployment, or broaden the acoustic variation available for training.
Reproduce difficult scenarios to investigate failures and compare model versions under consistent conditions. Clean references and structured metadata give your team the targets and context needed to measure performance, while simulation complements your real recordings.
How Treble Works
From your scenario to a repeatable dataset
Run the workflow from Python. Define the acoustic conditions, generate recordings and pass the resulting data into your existing training and evaluation pipelines.
Configure
Define the conditions your model needs to handle.
Select or import environments and assign acoustic materials. Define the speech and noise sources, microphone configuration and positions that represent your intended use.
Simulate
Model how sound reaches your microphones.
Generate room impulse responses using wave-based, geometrical or hybrid simulation. Capture how the environment changes the sound reaching your microphones.
Render
Create recordings with targets you can train and score against.
Run the generated audio through your evaluation pipeline and compare model versions under the same conditions. Investigate failures, identify gaps in training coverage, and complement testing on real recordings.
Scale, Evaluate, Train & Iterate
Expand coverage and compare model performance.
Run simulation batches across rooms and conditions in the cloud. Feed the rendered data into your training and evaluation pipelines, then adjust the scenario to investigate failures or fill gaps in coverage.
Key Capabilities
The controls behind the data
Better Design. Better Listening.
Lower Word Error Rate
0%
Lower source distance estimation error
0%
Lower source localization error
0%
Resources and validations
Improving multichannel speech enhancement through accurate room-acoustic simulations
Training on the high-fidelity dataset results in an up to 38% relative reduction in median word error rate compared to the lower-fidelity alternatives. These results show that augmentation with high-fidelity room-acoustic simulations directly translates into improved multichannel speech enhancement performance.
ML model evaluation using synthetic acoustic data
By evaluating against synthetic acoustic datasets with known parameters, you can assess algorithm performance systematically across a wide range of conditions without physical measurement.
Speech enhancement, recognition and separation
We demonstrate the higher accuracy of our IRs by comparing with recorded IRs from complex real-world environments.
Frequently Asked Questions
Yes. Treble provides a Python based interface that integrates directly into existing machine learning workflows and automation pipelines.
Treble supports workflows for speech enhancement, source localization, blind room estimation, denoising, adaptive audio, conversational AI, foundation models, and many other audio AI applications.
