External
Captioning Quality Audit
Quality is front and centre of everything we do at Ai-Media. Ai-Media’s caption quality is based on a robust, measurable and verifiable system consistent with international best practice.
Our caption quality is regularly measured by way of independent quality assessment, using NER* methodology. Audit reports are made available to our clients, and underpin our commitment to drive continuous improvement.
| Quarter ended – Year | Number of programs sampled | NER score Average | Uptime greater than 99.9% |
|---|---|---|---|
| Sep-22 | 13 | 98.4 |  |
| Jul-22 | 13 | 99.3 | |
| Mar-22 | 13 | 99.5 | |
| Dec-21 | 13 | 99.5 | |
| Sep-21 | 13 | 99.4 | |
| Jun-21 | 13 | 99.2 | |
| Mar-21 | 13 | 99.4 | |
| Dec-20 | 13 | 99.5 | |
| Sep-20 | 13 | 99.8 | |
| Jun-20 | 13 | 99.5 | |
| Mar-20 | 13 | 99.6 | |
| Dec-19 | 13 | 99.5 | |
| Sep-19 | 13 | 99.3 | |
| Jun-19 | 13 | 99.4 | |
| Mar-19 | 13 | 99.4 | |
| Dec-18 | 13 | 99.5 | |
| Sep-18 | 13 | 99.5 | |
| Jun-18 | 13 | 99.5 | |
| Mar-18 | 13 | 99.3 | |
| Dec-17 | 13 | 99.5 | |
| Sep-17 | 13 | 99.5 | |
| Jun-17 | 13 | 99.4 | |
| Mar-17 | 13 | 99.6 | |
| Dec-16 | 13 | 99.6 | |
| Sep-16 | 13 | 99.4 | |
| Jun-16 | 13 | 99.5 | |
| Mar-16 | 13 | 99.6 | |
| Dec-15 | 13 | 99.6 | |
| Sep-15 | 13 | 99.3 | |
| Jun-15 | 13 | 99.4 | |
| Mar-15 | 13 | 99.3 | |
| Dec-14 | 13 | 99.3 | |
| Sep-14 | 13 | 99.2 | |
| Jun-14 | 13 | 99.5 | |
| Mar-14 | 13 | 99.1 | |
*The NER model is a method of calculating the accuracy (quality) of live captions. NER stands for; Number, Edition error, and Recognition error.
A NER value of 100 indicates that the content was captioned entirely correctly.
The NER score can be calculated using the following formula:
N (Number) = Total number of words in the live captions.
E (Edition error) = Edition error
R (Recognition error) = Recognition error
