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Word Error Rate (WER)
Word Error Rate (WER) is a common metric of the performance of an automatic speech recognition (ASR) system.
The general difficulty of measuring the performance of ASR systems lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level.
This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate (see this article for further information).
Word error rate can then be computed as:
WER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).
Related use cases :
Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence Models
Uploaded on Nov 1, 2022Sequence-to-sequence models, such as attention-based models in automatic speech recognition (ASR), are typically trained to optimize the cross-entropy criterion which correspon...
Word Error Rate Estimation Without ASR Output: e-WER2
Uploaded on Nov 1, 2022Measuring the performance of automatic speech recognition (ASR) systems requires manually transcribed data in order to compute the word error rate (WER), which is often time-co...
Improving Sign Language Translation with Monolingual Data by Sign Back-Translation
Uploaded on Nov 1, 2023About the metric
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