In AI, hallucinations refer to outputs that are fabricated or inaccurate — the system “imagines” information that wasn’t present in the input.
What is an example of hallucinations?
An ASR system might incorrectly transcribe background noise as meaningful speech, or an enhancement model might add non-existent speech artifacts.
How do hallucinations work?
They typically result from overfitting, ambiguous inputs, or model biases, where the system guesses patterns based on prior training.
How does ai-coustics use hallucinations?
We minimize hallucinations through careful dataset design, model evaluation, and quality metrics to ensure enhanced audio remains faithful to the original speech. Read more about our approach to hallucinations here.
