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% |
---|---|---|---|
Dec-22 | 13 | 99.2 | ![]() |
Sep-22 | 13 | 99.5 | ![]() |
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
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