AI intelligence might be great, but is it good enough for captions?  AI makes it possible for machines to learn from experience and it also gives them the chance to adjust to inputs. It also means that machines can perform human-related tasks while also learning from experience. That brings about the question- is there ever going to be a fully automated solution which would benefit closed captioning?

The State of AI

In recent years, it looks like there has been a rise in digital assistants such as Alexa, Siri and even chatbots. There is also much more reliance on text messages, speech recognition and more. One area of high-achievement for AI is ASR. This is when voices are automatically transcribed into the written word. If you want to find out more read on.

What is ASR?

If ASR is to work then automatic captioning software has to be programmed so that it can predict and then deliver all of the possible outcomes. There has been a lot of success in various applications, for example, if you look at voice-assistants such as Siri or even Alexa, you will soon see that they work very well. The vocab stance is very task and command. When you look at bigger vocab sizes, you will soon see that this has posed as somewhat of a challenge.

ASR for Captioning

Before we delve into the world of captioning, let’s look at self-driving vehicles. There have been notable failures here and a lot of it comes down to unexpected visual input. This is what happened with the infamous tagging error which was made by Google. If you think about programming a car, you will soon see that it is very difficult, if not near-impossible to predict every single situation that the car might come across. Some might say that the same can be applied to ASR. As the vocab size grows, the task-at-hand becomes much more complex. There have been so many improvements for ASR but the captioning task is now much more complicated. That being said, machines are now more developed and now things look to be stronger than ever within the industry.

Of course, there are a lot of advancements to be made. For example, when you look at the different accents that people have, you will soon see that computer software can find it hard to understand or differentiate words. Some may see this as a failure on the AI’s part, but you need to remember that the machine is always learning and expanding its knowledge and database. This means that it is more than possible for the computer to adjust and learn these accents for the future. This is already happening within the AI and transcription industry, and it’s remarkable to say the least.

So, when you look at how the industry has changed overall, you will soon see that huge advancements have been made and that it is great to see so many companies thrive as a result.