What is Video Transcription and What is it Used For?

What is Video Transcription?

The process of converting the speech in a video to text is called video transcription. This can be done with automatic speech recognition technology, a human transcriptionist, or the best of all, combining the two. Transcription can also be applied to any audio recordings, 911 calls, call centers recordings and others.

Speech Recognition Technologies

As a cross-disciplinary subfield of computer science and computational linguistics, automatic speech recognition (ASR) formulates methodologies and technologies that allow for the recognition and translation of spoken languages into text. It is also sometimes referred to as Speech to Text (STT). The science integrates knowledge and research in computer science, linguistics, and computer engineering.

Many types of speech recognition systems require ‘training.’ Like many forms of AI, training helps computer systems with their perception or recognition through the development of patterns. Training a speech recognition system is also known as ‘enrollment.’

When a system is undergoing enrollment, an individual speaker reads text or isolated vocabulary into the system. The system uses machine learning methods to analyze the specific voice and speech patterns and uses that data to fine-tune its recognition of that person’s speech. Over time, the system actually improves, resulting in increased accuracy.

There has been a long history of trial and error with several significant waves of innovations in speech recognition technology. As big data, machine learning, and deep learning have advanced, so has speech recognition. In the last twenty years, there has been an upsurge in both academic papers and white papers published on the advancement of technology. Many areas of industry and ‘smart’ household advances are now adopting a variety of uses for speech recognition.

Speech Recognition Application

Not everyone cares to understand how the programming works as long as Alexa can understand them and direct them to the nearest coffee shop. Due to these advances, though, we can talk to our cars, use speech to text on our cell phones, and ask our Google assistant how many miles it is to the moon.

What has brought the advances in speech recognition along in leaps and bounds in recent years is a deep learning method called Long short-term memory (LSTM). No, this isn’t when you forget where you left your cell phone and run to ask Alexa to find it. LSTM is a recurrent neural network (RNN) that was initially published by Sepp Hochreiter & Jürgen Schmidhuber in 1997. RNNs have the capacity for “Very Deep Learning” functions that necessitate memories of events that may have occurred several thousand steps back. This “learning” or “memory recall” is hugely significant for speech recognition to work well.

As with all AI systems with deep learning capacity, as they ‘learn’ and receive more input, the system has the ability to improve with time. With speech recognition, having input from various users with different dialects helps the system learn to identify other ways people say the same word. “Tomāto, tomăto.”

Over time, with training around 2007, Connectionist Temporal Classification (CTC) found that their trained LSTM began to surpass many other established speech recognition systems in specific applications. It wasn’t long after that Google jumped into the speech recognition industry with full force, using CTC to train their system, and reported a dramatic 49% increase in performance. Now, you carry this technology everywhere you go through Google Voice on your smartphone.

Quality and Editing

From the top, the suggestion for the best option for quality video transcription is a ‘combination’ of quality automatic speech recognition technology and a human transcriptionist. I think you know why. How many times have you been using your smartphone’s voice-to-text technology and were grateful you checked it before you hit ‘send.’ Even Google’s Voice makes errors… often. The better quality speech recognition software you invest in, the fewer mistakes can happen, but at this time, no speech recognition software is perfect.

Using only a human transcriptionist is tedious and costly. It can be very time-consuming to review tapes of audio and type them out manually. It can be done and done well, but the overall cost when considering working hours, benefits, and more – can be enormous. This is why the best option is to combine the efforts of a top-quality speech recognition application with a human transcriptionist to make reviews and edits.

Take a look at the video below to see how you can use the technology to automatically and accurately transcribe complex video from a senate committee hearing with multiple speakers, including Mark Zuckerberg in this short 3-minute video. Then, see how easy it is to generate a printable transcript with timestamps and speaker identifiers and extract the text to use for reports, captions, and redaction. Please watch it in full screen and HD to see all the details. The video used CaseGuard software to do the automatic transcription.

Why Video Transcription?

There are many reasons why we use video transcription. Many court cases are held via video conference, and a written record must be made of what happened in the court proceedings. A business meeting can be recorded via video, and a transcript can later be produced so that all attendees have notes. A closed captions to a news conference, classroom, movie, and surveillance videos. It can mean greater access to those who are hard of hearing or have other difficulties. The reasons to use video transcription can be endless.

Transcription and Privacy

One of the most important reasons why you would want to transcribe your video or audio files is to analyze the speech and recognize all the private, personal, and confidential information that was mentioned in the speech and be able to redact them from the video or audio recordings by muting or bleeping the information. Take a look at the video below and notice how using such a technology will require a minimum amount of work and can achieve so much in a matter of seconds. Try to watch it in full screen and HD to see the full details.

The automatic speech recognition, transcription and text analysis through deep learning and artificial intelligence can help remove all sensitive information from any video or audio file using AI redaction software. It can be done on one or a million files automatically.

Conclusion

At the end of the day, it does not really matter whether you are working in an IT department or for the government, law enforcement, in a hospital, call center, shopping center, bank or otherwise, taking advantage of the automatic speech recognition technology will have a huge impact on the way you do business and it can be one of the smartest decisions you could make. It will save a lot of time, effort and money.