A simple error classification system for understanding sources of error in automatic speech recognition and human transcription.

Objectives: To (1) discover the types of errors most commonly found in clinical notes that are generated either using automatic speech recognition (ASR) or via human transcription and (2) to develop efficient rules for classifying these errors based on the categories found in (1). The purpose of classifying errors into categories is to understand the underlying processes that generate these errors, so that measures can be taken to improve these processes.

Methods: We integrated the Dragon NaturallySpeaking v4.