Word Error Rate

Word Error Rate

Word error rate (WER) is a common metric of the performance of a speech recognition or machine translation system.

The general difficulty of measuring performance 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 instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. However, this kind of measurement does not give any details about the nature of translation errors. Therefore more work has to be done in order to identify the main issues and to focus the research efforts.

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.

Word error rate can then be computed as:

or

where

• S is the number of substitutions,
• D is the number of deletions,
• I is the number of insertions,
• C is the number of the corrects,
• N is the number of words in the reference (N=S+D+C)

When reporting the performance of a speech recognition system, sometimes word accuracy (WAcc) is used instead:

where

• H is N-(S+D), the number of correctly recognised words.

Note that since N is the number of words in the reference, the word error rate can be larger than 1.0, and thus, the word accuracy can be smaller than 0.0. This problem can be overcome by using the hit rate with respect to the total number of test-reference match pairs found by the matching process used in scoring, (H+S+D+I), rather than with respect to the number of reference words, (H+S+D). This gives the match-accuracy rate as MAcc = H/(H+S+D+I) and match error rate, MER = 1-MAcc = (S+D+I)/(H+S+D+I). WAcc and WER as defined above are, however, the de facto standard most often used in speech recognition.