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README.md
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title: WER
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emoji: 🤗
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colorFrom: blue
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colorTo: red
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sdk: gradio
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app_file: app.py
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pinned: false
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tags:
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- evaluate
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- metric
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# Metric Card for WER
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## Metric description
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---
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title: WER
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emoji: 🤗
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colorFrom: blue
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colorTo: red
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sdk: gradio
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app_file: app.py
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pinned: false
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tags:
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- evaluate
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- metric
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description: >-
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Word error rate (WER) is a common metric of the performance of an automatic
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speech recognition system.
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The general difficulty of measuring performance lies in the fact that the
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recognized word sequence can have a different length from the reference word
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sequence (supposedly the correct one). The WER is derived from the Levenshtein
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distance, working at the word level instead of the phoneme level. The WER is a
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valuable tool for comparing different systems as well as for evaluating
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improvements within one system. This kind of measurement, however, provides no
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details on the nature of translation errors and further work is therefore
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required to identify the main source(s) of error and to focus any research
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effort.
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This problem is solved by first aligning the recognized word sequence with the
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reference (spoken) word sequence using dynamic string alignment. Examination
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of this issue is seen through a theory called the power law that states the
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correlation between perplexity and word error rate.
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Word error rate can then be computed as:
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WER = (S + D + I) / N = (S + D + I) / (S + D + C)
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where
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S is the number of substitutions,
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D is the number of deletions,
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I is the number of insertions,
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C is the number of correct words,
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N is the number of words in the reference (N=S+D+C).
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This value indicates the average number of errors per reference word. The
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lower the value, the better the
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performance of the ASR system with a WER of 0 being a perfect score.
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---
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# Metric Card for WER
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## Metric description
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