Measuring Quality: Word Error Rate Explained

Word Error Rate (WER) is a commonly used metric for measuring the quality of speech recognition models, specifically automated speech recognition (ASR). It calculates the number of errors made by an ASR model in transcribing audio to text. The formula for WER involves counting the number of word insertions, deletions, and substitutions made by the model compared to a ground-truth transcript, then dividing this sum by the total number of words in the ground-truth. A lower WER indicates better performance. However, while WER is useful for comparing ASR models, it doesn't provide a comprehensive understanding of how well a model will perform on specific types of data or with certain vocabulary.