Metric: cer
Update on GitHub


Character error rate (CER) is a common metric of the performance of an automatic speech recognition system. CER is similar to Word Error Rate (WER), but operate on character insted of word. Please refer to docs of WER for further information. Character error rate can be computed as: CER = (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 characters, N is the number of characters in the reference (N=S+D+C). CER's output is always a number between 0 and 1. This value indicates the percentage of characters that were incorrectly predicted. The lower the value, the better the performance of the ASR system with a CER of 0 being a perfect score.

How to load this metric directly with the datasets library:

from datasets import load_metric
					metric = load_metric("cer")


    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}