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title: CER
emoji: 🤗
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 3.0.2
app_file: app.py
pinned: false
tags:
- evaluate
- metric
Metric Card for CER
Metric description
Character error rate (CER) is a common metric of the performance of an automatic speech recognition (ASR) system. CER is similar to Word Error Rate (WER), but operates on character instead of word.
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).
How to use
The metric takes two inputs: references (a list of references for each speech input) and predictions (a list of transcriptions to score).
from evaluate import load
cer = load("cer")
cer_score = cer.compute(predictions=predictions, references=references)
Output values
This metric outputs a float representing the character error rate.
print(cer_score)
0.34146341463414637
The lower the CER value, the better the performance of the ASR system, with a CER of 0 being a perfect score.
However, CER's output is not always a number between 0 and 1, in particular when there is a high number of insertions (see Examples below).
Values from popular papers
This metric is highly dependent on the content and quality of the dataset, and therefore users can expect very different values for the same model but on different datasets.
Multilingual datasets such as Common Voice report different CERs depending on the language, ranging from 0.02-0.03 for languages such as French and Italian, to 0.05-0.07 for English (see here for more values).
Examples
Perfect match between prediction and reference:
from evaluate import load
cer = load("cer")
predictions = ["hello world", "good night moon"]
references = ["hello world", "good night moon"]
cer_score = cer.compute(predictions=predictions, references=references)
print(cer_score)
0.0
Partial match between prediction and reference:
from evaluate import load
cer = load("cer")
predictions = ["this is the prediction", "there is an other sample"]
references = ["this is the reference", "there is another one"]
cer_score = cer.compute(predictions=predictions, references=references)
print(cer_score)
0.34146341463414637
No match between prediction and reference:
from evaluate import load
cer = load("cer")
predictions = ["hello"]
references = ["gracias"]
cer_score = cer.compute(predictions=predictions, references=references)
print(cer_score)
1.0
CER above 1 due to insertion errors:
from evaluate import load
cer = load("cer")
predictions = ["hello world"]
references = ["hello"]
cer_score = cer.compute(predictions=predictions, references=references)
print(cer_score)
1.2
Limitations and bias
CER is useful for comparing different models for tasks such as automatic speech recognition (ASR) and optic character recognition (OCR), especially for multilingual datasets where WER is not suitable given the diversity of languages. However, CER provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.
Also, in some cases, instead of reporting the raw CER, a normalized CER is reported where the number of mistakes is divided by the sum of the number of edit operations (I + S + D) and C (the number of correct characters), which results in CER values that fall within the range of 0–100%.
Citation
@inproceedings{morris2004,
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.}
}