Artificial Intelligence is one disruptive technology that has changed the way valuable data is handled. Machine learning is considered at its best when it is working with large analyzable sets of data more like text. But a majority of the data that is available is not in the text form because they are also in the form of spoken words on videos, audio recordings, or even live events. This makes Machine Learning an important goal for reliable voice transcription.

Transcription is the process of converting audio or video content into text for different purposes. In the world of business, transcription finds path-breaking applications in different sectors including medical, legal, music, and much more. Any business which needs to document effective communication finds transcription extremely useful yet, without being empowered by AI-based technologies like ML, it can go awry.

Voice to text transcription has been for long an important business based on its own merits and has traditionally been done by a team of human transcribers who charge $3-$4 per minute to do it. As per the Bureau of Labor Statistics, there were about 57,400 medical scribes and 19,600 court reporters in the United States in 2016. Grand View Research projects that the global voice recognition market will hit an overall figure of $127.58 billion by 2024.

Why is Machine Learning important?

Manual transcription recounts as a large loss of time and energy especially with large amounts of data involved. Manual transcription involves the provision of aggressively intensive training to a transcriptionist to ensure accuracy is achieved. A major drawback of manual transcription is that humans are unable to manage multiple accents hence the accuracy is dependent on the individual transcriber’s accent limitations.

When such drawbacks have hit the transcription industry, Machine Learning comes as a respite because it provides tools and technologies that convert speech to text. This has indeed saved a lot of human effort and time and eliminated the limitations of manual transcription. Transcription can be further divided into verbatim or intelligence. By verbatim, we mean a word-by-word transcription of the audio file without any changes. This can be easily done by software too but intelligent transcription can only be done by Machine Learning which is a step ahead. The texts become more accurate than dictation because ML makes grammatical correction as and when needed.

ML applications aid the editors to improve texts by identifying patterns and learnings thanks to its auto-suggest features, autocomplete features and even paraphrasing suggestions.

Understanding transcription in different industries

1.Law Firms, Paralegals, Court Reporters, and Attorneys

Some of the leading industries that use transcriptions for legal transcription services are law firms, paralegals, court reporters, and other legal professionals. Whether it is depositions or court hearings, audio or video footage as evidence or witness statements. Most of the legal activities are transcribed often to make them more accessible to lawyers, jurors, and judges.

There is a lot of paperwork involved in legal procedures that include lengthy and never-ending petitions, status records, and much more. Every legal proceeding has to be recorded in some manner so that it can be utilized with ease in the future. Litigations even go on for years and if the proceedings in the court go without keeping proper records it can go out of hand. Legal officers and professionals do not have enough time to note down everything hence the relevant information is noted down in voice files which can be transcribed using ML.

2.Music / TV & Film Industry

With the music industry embracing technology at all levels to create great songs, it should not be forgotten that songs are also transcribed and converted into text and then stored. Text subtitles in movies are created when voices are converted into text thanks to transcription. There is also something called production transcripts which are verbatim transcription or text conversion of either video or audio or both. In the TV/Film industry, this is a common need much fulfilled by ML-based technologies.

3.Medical industry

Medical information of patients has to be recorded well so that it can take care of insurance and medical history needs. Since the documentation of all patient information is needed in the patient’s file, the recording as well as the transcription of all procedures notes, and related notes and materials are extremely important to proceed. Hence doctor’s records, consultation summary, and step-by-step surgical procedures are recorded into audio files through dictations. Earlier these files were converted by humans or by software but were prone to errors. But with ML, there is a lot of intelligence and sense involved in converting the files into text-speech. 

The use of ML in medical industry transcription has become a lucrative industry thanks to the volumes of medical records and data being generated each day.

4.Education industry

The transcription of materials in the academic world benefits the education industry stakeholders like students and academicians alike. This includes lectures, seminars, videos, and other source materials for research papers and interviews. The pandemic has also ensured that universities and colleges go online and are increasingly offering lecture transcripts all thanks to transcription solutions at play.

5.Benefits of ML for the transcription industry

Now that we have a good idea about ML, transcription services, and its applications, it is important to note what benefit it entails.

1.Saves time and cost

Human transcriptionists don’t come cheap. They charge a good amount in addition to training them which in itself is expensive. With time when the transcribers become more skilled and deliver speed with accuracy, they become heavy on the budget. But switching to Machine-learning based tools and technologies can convert large volumes of work in a shorter time because they take less time and are far more accurate. With time more and more can be produced and less human involvement will be the result. A single human editor can check and even edit ML transcribed work instead of employing many transcribers, editors, and proofreaders.


With the use of Machine Learning transcription becomes a process that is entirely automated. Minimal human intervention is required because the voice content is automatically converted to text by ML transcription software. In order to ensure that the transcription conversion is accurate, these files are further proofread and edited by humans. Manual work is reduced and it is much easier, less time-consuming to edit than to do it with scratch.


ML can help businesses to transcribe the voice files with ease anytime. Manual transcription for this level of ease and accuracy requires skilled and trained transcribers. Added to this businesses need to send the work to professional transcription organizations or freelancers on a day-to-day basis adding burden to work. The best part about using ML-based transcription is that the software is very easy to use and without much training and knowledge, one can use it.

4.Smooth business communication

The ML transcription software custom made by a Machine learning solutions company for automatically transcribing emails and meeting minutes can boost business communication. This also ensures confidentiality as people do not need human assistants to transcribe their sensitive communication. ML software applications provide autocorrect, autocomplete, and auto-suggest features so that accuracy is at its heights.

5.It gets better with time

ML is an intelligent software and with time it gets better at recognizing patterns and trends. It keeps getting better as time passes. For instance, in the case of medical transcription, ML software can do good by handling a wide range of accents and dictators with extreme ease. It also memorizes standard phrases in medical parlance or by a specific doctor reducing the need for a human editor.

A concluding note

ML is a trans-formative technology that has ensured transcription becomes lucrative with time. As time progresses, machines get trained by learning which confirms further accuracy. ML all across the world is saving time, costs, and efforts. It is literally transforming and reshaping the transcription industry and will continue to do so.