The first step came over ten years ago with the launch of captions. And in an effort to scale this technology, automated captions came a few years later. The success of that effort has been astounding, and a few weeks ago we announced that the number of videos with automatic captions now exceeds 1 billion. Moreover, people watch videos with automatic captions more than 15 million times per day. And we have made meaningful improvements to quality, resulting in a 50 percent leap in accuracy for automatic captions in English, which is getting us closer and closer to human transcription error rates.
But there is more to sound and the enjoyment of a video than words. In a joint effort between YouTube, Sound Understanding, and Accessibility teams, we embarked on the task of developing the first ever automatic sound effect captioning system for YouTube. This means finding a way to identify and label all those other sounds in the video without manual input.
We started this project by taking on a wide variety of challenges, such as how to best design the sound effect recognition system and what sounds to prioritize. At the heart of the work was utilizing thousands of hours of videos to train a deep neural network model to achieve high quality recognition results. There are more details in a companion post here.
As a result, we can now automatically detect the existence of these sound effects in a video and transcribe it to appropriate classes or sound labels. With so many sounds to choose from, we started with [APPLAUSE], [MUSIC] and [LAUGHTER], since these were among the most frequent manually captioned sounds, and they can add meaningful context for viewers who are deaf and hard of hearing.
So what does this actually look like when you are watching a YouTube video? The sound effect is merged with the automatic speech recognition track and shown as part of standard automatic captions.
Click the CC button to see the sound effect captioning system in action
We are still in the early stages of this work, and we are aware that these captions are fairly simplistic. However, the infrastructural backend to this system will allow us to expand and easily apply this framework to other sound classes. Future challenges might include adding other common sound classes like ringing, barking and knocking, which present particular problems -- for example, with ringing we need to be able to decipher if this is an alarm clock, a door or a phone as described here.
Since the addition of sound effect captions presented a number of unique challenges on both the machine learning end as well as the user experience, we continue to work to better understand the effect of the captioning system on the viewing experience, how viewers use sound effect information, and how useful it is to them. From our initial user studies, two-thirds of participants said these sound effect captions really enhance the overall experience, especially when they added crucial “invisible” sound information that people cannot tell from the visual cues. Overall, users reported that their experience wouldn't be impacted by the system making occasional mistakes as long as it was able to provide good information more often than not.
We are excited to support automatic sound effect captioning on YouTube, and we hope this system helps us make information useful and accessible for everyone.
Noah Wang, software engineer, recently watched "The Expert (Short Comedy Sketch)."
