Training Loss is a metric that measures how far an AI model's predictions differ from the expected outputs during training. Lower loss generally indicates better model learning.
Voice AI engineers monitor Training Loss while developing speech recognition, speech synthesis, and language models to evaluate learning progress, detect overfitting, and optimize model performance.