The Evolution of Transcription Technology

By: Verbit Editorial



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Millions of audio and video files are produced every day by businesses and organizations in virtually every industry. In fact, the sheer volume of unstructured data has increased to the point that analysts at Gartner estimate that upwards of 80% of all organizational data exists in these types of formats. In this context, where there is a clear shift from written content to audio and video, the need for transcription technology has never been more critical.

When is Transcription Required?

For many businesses, transcription of audio files is a necessary element of the core process, further enforced by a variety of legal regulations. For example, in higher education, Titles II and III of the Americans with Disabilities Act as well as Sections 504 and 508 of the Rehabilitation Act mandate that all videos be captioned for complete accessibility. In entertainment and media, the FCC Closed Captioning Regulations and 21st Century Communications and Video Accessibility Act require that captions be highly accurate so that individuals who are d/Deaf or hard of hearing can consume content without any barriers. In the legal industry, all court proceedings, as well as activities that take place in law firms, must be accurately documented via legal transcription. Therefore, in these industries especially, speech transcription software is essential.  

How Transcription Technology Has Changed

Before the advent of digital transcription software, the task of converting speech to text was accomplished manually. Individuals who were charged with transcribing audio had to listen to the speaker and type out what they heard. This presented a number of challenges, as prior to the development of recording technologies, the transcriber had to be physically located where the service was required. Due to the manual nature of the work, advanced skills, like shorthand, were required to get the job done. Once basic recording tools, such as cassette tapes and portable recorders, were developed and made more available, the work became much more flexible and new possibilities emerged. Transcribers could now work remotely and take on more jobs for various types of businesses.

As the field developed further, specialized transcription equipment began to emerge. One such example is the transcription foot pedal, a device that allows a transcriber to control the playback of the audio, and advance or rewind the recording with their foot, leaving their hands free to keep typing.  

With the evolution of modern technology, including speech recognition, the task of transcription became even easier and more accessible. Audio recordings can now be made using more sophisticated tools for clearer sound, and files can be converted to different types and then stored locally on a device, or in the cloud.

With the advancement of technology came more advanced digital transcription software. Today’s state-of-the-art speech digital transcription equipment includes a variety of sophisticated features, including text expanders, term search, built-in spelling and grammar checks, and even audio equalizers to reduce background noise and control bass/treble.

Despite these advancements, most professional transcription providers today continue to rely on manual methods, resulting in expensive service costs and long turnaround times. This approach also limits scalability, operating at smaller volumes for lower profit margins.

Furthermore, the few organizations that do utilize digital transcription software only reach an average accuracy rate of 70-80%, falling short of the quality standards for many industries, where, in some cases, even 95% is insufficient to convey complex material. What separates the leaders from the rest of the pack? The answer lies in AI technology.

AI-Powered Transcription Technology

Propelled by the advancement of sophisticated AI technologies, the speech-to-text market is currently experiencing a boom. In fact, the $55-billion speech recognition industry has been forecasted to grow 11% from 2016 to 2024.

AI-driven speech recognition technology works using algorithms through acoustic and linguistic modeling. Acoustic modeling refers to the connection between linguistic units of speech and audio signals, while language modeling matches those sounds with word sequences to differentiate between similar-sounding words.

A key innovation that has spurred the evolution of dictation transcription software is the introduction of context-focused algorithms. It can often be hard to differentiate between two similar-sounding phrases without any background information. However, if the speech-to-text engine is fed with data about the particular subject matter, it can accurately convert the spoken words to text with minimal errors. Going forward, more available data and context-focused algorithms will result in better predictive models and even higher accuracy levels, with minimal need for human intervention.

Although speech recognition technology has come a long way, there is still room to grow. One area that offers significant room for improvement is in accent recognition. Speech analytics is another field that holds a lot of promise for future advancement. Turning unstructured data such as audio and video files into structured, text-based information will enable greater pattern recognition and the extraction of practical insights which is sure to have a great impact on many industries. As the amount of unstructured data continues to grow, digital transcription systems that are powered by AI technology stand to revolutionize the speed and accuracy of workflows in a variety of industries.