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.

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.[1]

Word error rate can then be computed as:

\mathit{WER} = \frac{S%2BD%2BI}{N}

or

\mathit{WER} = \frac{S%2BD%2BI}{S%2BI%2BC}

where

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

\mathit{WAcc} = 1 - \mathit{WER} = \frac{N-S-D-I}{N} = \frac{H-I}{N}

where

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.

Other metrics

One problem with using a generic formula such as the one above, however, is that no account is taken of the effect that different types of error may have on the likelihood of successful outcome, e.g. some errors may be more disruptive than others and some may be corrected more easily than others. These factors are likely to be specific to the syntax being tested. A further problem is that, even with the best alignment, the formula cannot distinguish a substitution error from a combined deletion plus insertion error.

Hunt (1990) has proposed the use of a weighted measure of performance accuracy where errors of substitution are weighted at unity but errors or deletion and insertion are both weighted only at 0.5, thus:

\mathit{WER} = \frac{S%2B0.5D%2B0.5I}{N}

There is some debate, however, as to whether Hunt’s formula may properly be used to assess the performance of a single system, as it was developed as a means of comparing more fairly competing candidate systems. A further complication is added by whether a given syntax allows for error correction and, if it does, how easy that process is for the user. There is thus some merit to the argument that performance metrics should be developed to suit the particular system being measured.

Whichever metric is used, however, one major theoretical problem in assessing the performance of a system, is deciding whether a word has been “mis-pronounced,” i.e. does the fault lie with the user or with the recogniser. This may be particularly relevant in a system which is designed to cope with non-native speakers of a given language or with strong regional accents.

The pace at which words should be spoken during the measurement process is also a source of variability between subjects, as is the need for subjects to rest or take a breath. All such factors may need to be controlled in some way.

For text dictation it is generally agreed that performance accuracy at a rate below 95% is not acceptable, but this again may be syntax and/or domain specific, e.g. whether there is time pressure on users to complete the task, whether there are alternative methods of completion, and so on.

The term "Single Word Error Rate" is sometimes referred to as the percentage of incorrect recognitions for each different word in the system vocabulary.

See also

References

  1. ^ University of Toronto Libraries