The experience of watching videos without captions is similar to playing the violin in an empty auditorium without any people present. You can miss out on a huge potential if you fail to add captions to your video, regardless of how well you create your video.
It is by no means an option anymore to ignore the importance of captioned content when we live in a world that is heavily reliant on the internet.
Closed captions and subtitles are an important marketing tool that can be incorporated into your video and ensure its success. You have seen this time and time again. Do you know what lies behind the technology side of things? If not, then what do you know?
Have you ever wondered about the technology that is used by subtitling or transcription software to convert audio into text? Do you ever wonder about the technology that makes it possible?
An overview of five technologies that video captioning uses to bring a greater level of engagement and to enhance the number of views a video receives can be found in this article.
Here are a few of the technologies that can help you increase the reach of your videos with these technologies.
5 videocaptioning technology
In order to enhance engagement with the video captioning process, there are five technologies that can be used:
Using automatic speech recognition (ASR) is becoming increasingly popular
The mechanism by which subtitling and audio-to-text transcription software work is based on automatic speech recognition, which is a type of speech recognition technology. With speech recognition technology, the speech is broken down into bits that can be interpreted by the technology, converted to a digital format, and then finally analyzed by the technology. Among the best examples of automatic speech recognition is Apple’s Siri or Google’s Alexa, both of which are capable of converting audio into text, which can later be translated into a language of your choice using a spell checker or interpreter.
Interestingly, many automated speech recognition systems (ASRs) use artificial intelligence (AI) to convert audio into text in an accurate manner. A machine-learning algorithm works by converting speech into a format that can be easily understood by a machine. In today’s age of technology, these artificial intelligence solutions no longer require the user to speak clearly in order to function. In the era of smart AI systems, accents, dialects, and natural voice can be easily transcribed by more advanced AI systems.
It is advisable to use transcription and captioning software since manual video captioning normally takes 5-10 times as long as the video itself.
As a leading company in audio-to-text transcription, Amberscript uses ASR technology, which is a speech-to-text transcription technology, in order to translate spoken words into text. It utilizes artificial intelligence to provide the tool with a fast turnaround time while maintaining a high level of quality.
An audio recognition system is a technology that recognizes audio signals
There is also the critical aspect of being able to separate the actual sound from the actual speech, which is a critical aspect of captioning, transcription, and subtitles. Among the many things that make transcription software successful and widely used in many different industries is the ability of it to distinguish between crowd cheering, traffic noise, ball hitting noise, and even baby crying sounds, which can give a false impression that the voice is natural.
Companies are working overtime to develop audio or speech recognition technologies and are burning the midnight oil to develop them. There are some sounds that are not necessarily a word, and this technology can assist in understanding these sounds. To provide accurate results, the program needs to be able to discern between noise and existing languages at the same time.
Trint offers fully automated audio transcription software that is AI-based that is 99% accurate at converting audio files into text. Among the distinguishing features of Trint is the way it uses its technology to match the sounds of the words in the dictionary to their corresponding words in the editor, so that it can display those words in context.
Identification of a language
There is usually only one language used in recording and trancribing audio and video content. Content can often be presented in a mix of two languages or three languages, depending on the situation. During a panel discussion, for instance, some panelists may use a few words from their regional language during the course of the discussion.
During such a scenario, a transcription software that can translate audio files to text should be capable of detecting and identifying different languages. Subtitles and video captions can be made more accurate if they can detect changes in language and then accurately convert them into a desirable language.
Language identification can be used to distinguish one video captioning or transcription software from another, even though there may only be a few instances in which this happens.
Diarization
It is also essential to understand that a video captioning or audio-to-text software will even focus on another area, and that is diarization, which is the capability of the software to differentiate between two speakers. There is always the possibility that you will have more than one speaker in a panel discussion, for example. There are times when one person speaks, and there are times when another person speaks and contradicts the person speaking.
When it comes to understanding an accent or dialect, it is essential to be able to separate the speakers. Also, it is imperative that accuracy is maintained at all times. By using the diarylation technique, the software is able to identify when a new speaker starts speaking, make entries for breaks, and understand when a break occurs.
As a result, it helps to distinguish between different speakers and adds appropriate punctuation marks to the speech the continuous evolution of technology, companies use the diarylation technique to note the speaker and associate them with a name.
As a result of this, transcription software is now capable of captioning audio files in an entirely new way.
AI vocabulary and context
The ability to understand what a speaker is saying is another area where video captioning software focuses. Despite the fact that AI vocabulary and context are not technologies, they are fundamental components of ASR processes. It is the job of AI software to match the speech in audio files with the grammar and vocabulary it already knows when it receives an audio file with AI capabilities. If the software encounters a word which does not appear in its AI vocabulary, the program tries to link it to one that it knows, and if it does not find a match, it tries to link it to the one that is present in its AI vocabulary.
What does the speaker mean when they say effect? Is it what the speaker means when they say affect? Is there a difference between here and hear when they say here? Would it be possible to clarify whether they are trying to say two, to, or both at the same time? As a result of the difficulty in distinguishing similar sounding words or homophones among the captions provided by a video captioning software, we believe that accuracy of captions is crucial.
In order to obtain the rights of homophones, it is crucial to understand the context in which they occur. There are even instances in which this can be applied to full sentences. Using AI-enabled video captioning software, you can set your video captioning software up to recognize both “He is a miner” as well as “He is a minor.” Although both of these sentences are correct, the captioning software needs to be told which one is appropriate for that particular context.
