This happens because the model learned to associate clean audio patterns with words and was never exposed to noisy conditions during training. Neural networks assume that test data looks like training data, and when noise changes that distribution, predictions break down.

If most training samples are clean, the model learns very fine-grained acoustic features that do not generalize well. In noisy environments, those features are masked, so the network cannot match what it learned.

The solution is to include noise augmentation during training, such as adding background sounds, reverberation, and random distortions. This teaches the model to focus on speech-relevant signals rather than fragile acoustic details.

Common mistakes: Training only on studio-quality recordings, no data augmentation for audio ,ignoring real-world noise patterns

The practical takeaway is that robustness must be trained explicitly using noisy examples.