Why does a statistical language model built from a corpus of transcribed speech consistently misinterpret disfluencies (e.g., 'um', 'uh') as meaningful content rather than noise?

💡 Explanation

The language model interprets disfluencies as meaningful content because the training corpus lacks detailed annotation indicating these elements should be treated as noise; therefore, the model learns their statistical properties and integrates them, rather than filtering them as non-lexical items during training.

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