Model adaptation
Tailoring models for your application is the best way to ensure you get the
best possible results for your needs. For speech-to-text applications,
high
accuracy is essential to
maximize your
ROI, as to a first
approximation, the cost of using automatic transcriptions in your
workflow is proportional to the system's error rate. Therefore using a
system with a 90% accuracy (i.e. 10% error) may cost you twice that
of using a system with a 95% accuracy (i.e. 5% error).
We offer a model customization service to reach this goal in case the
generic model is not well suited to your data. Depending on the
available data, we can adapt the whole model, or only part of the model
such as the pronunciation model, the language model, and the acoustic
model. Therefore we can provide accurate tailored solutions that
cannot be matched with general-purpose open-source solutions. We also
offer solutions optimized for low footprint and low power consumption
when needed.
Model development
We also offer a service to build models on demand in order to get the best possible
results on your data. Each application needs a specific development plan which
is taking into account the availability of linguistic ressources and the
specific constraints linked to the usage of these ressources. Our work always
starts with a careful study of your needs and of the data to be
processed. Then we may need to create or collect some specific linguistic
ressources. The rest of the process is the model optimization to maximise the
model accuracy on your data. It should be noted that we have demonstrated our
ability to get the best of the available language ressources using our
technology in our participations to many international challenges which we
won.
Security issues
All our AI models are developed using our own data or are adapted from
existing models that we have built in-house using that same data. This
approach eliminates the risks associated with publicly available or
outsourced AI models, which may contain biases, hidden
vulnerabilities, or even intentional backdoors (e.g., through data
poisoning). For critical operations, we use a glass-box approach,
where the sources of knowledge are broken down and clearly
defined. This allows for finer control over the models and helps
mitigate the risk of learning from corrupted data.
If you are interested in a particular language or technology please
use our
contact form or our
VoxSigma request form, or send a note
directly to
contact@vocapia.com.