Transcription vs. Speech Recognition: A Comparison
Speech recognition and transcription can be used to quickly convert spoken words, numbers, or acronyms to text. Speech recognition and transcription have many applications, most prominently used for healthcare documentation, legal document creation or legal proceeding logs, and to create video transcripts for education and entertainment. In each of these applications, transcription and speech recognition could both be used to achieve similar results – but there are pros and cons to both transcription and speech recognition. There is also a hybrid solution, where speech recognized text is further edited by a human.
Pros and Cons of Transcription
Transcriptionists listen to speech, then write or type what they hear into a written or printed format. Until speech recognition became available, transcriptionists or scribes were the standard for transcribing speech. Now that speech recognition can compete with (and in many cases outperform) transcription, it is slowly being phased out. Many industries and organizations still prefer or continue to use transcription because of its advantages, but others have moved on because of its disadvantages.
The ‘human’ element of transcription can be both an advantage and disadvantage of transcription. A transcriptionist is responsible for translating human speech to text. A human can easily recognize words across various accents and refer to the context of a sentence to fill in any inaudible gaps – or just mark completely unrecognizable speech as ‘inaudible’.
Although AI can perform some of these actions with speech recognition, humans can still outperform automated speech recognition systems in accuracy. The disadvantage to the ‘human’ element of transcription is speed. The job requires fast typists, but even the fastest typists in the industry would have trouble competing with automated speech recognition, which can recognize and output full sentences at a time rather than needing to manually type each word.
“While speech recognition is gaining popularity, transcriptions are here to stay, at least for now. Technology is not yet as advanced as it could be, and speech recognition still makes many mistakes that human insight would not make. For legal and medical transcription, for example, although there are known speech recognition tools for these fields, a human transcription has the specialized knowledge to excel in these fields. They possess advanced knowledge of the terminology, the medical or legal processes, and the relationships with lawyers or doctors that, to be honest, technology is yet to have."
- Ofer Tirosh, founder and CEO of translation company TomedesA disadvantage of transcriptionists is that the job requires a fairly unique skillset. Not only is a transcriptionist required to type quickly to efficiently convert speech to text, they must also have a working knowledge of any subject they are transcribing. For example, a transcriptionist with no medical knowledge will not be able to keep up with a physician as they dictate because a typical dictation contains medical terminology that would be difficult to spell or recognize for someone with no medical background. This applies to any field – even someone with the right typing skills, which is a small enough group as it is, would not be able to easily transfer to a new field of transcription without adequate training.
Pros and Cons of Speech Recognition
Speech recognition software can be much faster than human transcription. A transcriptionist must recognize a word, then manually type it, while speech recognition simply recognizes and instantly outputs the recognized word. This saves time on each word recognized.
Speech-to-text technology is widely accessible and speech recognition solutions designed for more specific purposes, such as legal or medical speech recognition, are also available. Some free speech recognition software also exists. However, free automated speech recognition systems has more limitations.
Beyond accessibility, there are plenty more advantages to using speech recognition, including heightened efficiency and cost savings, but there are also disadvantages to speech recognition. Speech recognition competes with transcription on accuracy and speed. Accuracy is the key component of any speech recognition solution.
Although speech recognition accuracy has come a long way, think of it this way: speech recognition is a computer system competing with humans to better recognize human speech. Consistent, 100% accuracy isn’t possible for either group, but transcriptionists can still outperform speech recognition on accuracy. Speech recognition, however, is almost always faster than human transcription.
To improve speech recognition accuracy, the software must recognize and assess the context of each word in a sentence and, for more specialized speech-to-text, recognize the subject matter as well. Accuracy can be even further enhanced by artificial intelligence and machine learning.
Speech recognition can also be limited by its ease-of-use. Speech recognition should be used to transcribe speech more efficiently and cost-effectively – it should never stand in the way of a more efficient workflow.
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Transcription vs. Speech Recognition in Healthcare
Speech recognition has become a widely-adopted solution for medical documentation. Many facilities utilize some level of speech recognition, partly because of the speed allowed by speech to text software. Transcription and speech recognition are primarily used for completing documentation for patient visits or any other reporting in healthcare. A large part of a healthcare provider’s responsibility is proper documentation, and there are a few common approaches to this: manual entry, speech recognition, transcription, or a hybrid solution.
Manual entry involves completing documentation by hand. Since documentation is now completed entirely online, either on a desktop or mobile device, this involves manually clicking through required fields and typing required information.
With speech recognition and transcription, this required information is instead spoken. In the case of transcription, the dictation is transcribed by another human, whereas speech recognition transcribes the text through artificial intelligence. Certified medical transcriptionists listen to dictations from healthcare providers and transcribe those dictations to text for medical reports.
A hybrid solution involves a combination of any of the three prior methods to keep providers comfortable and efficient. This could mean filling some text fields or checking some boxes manually and using speech recognition for the remainder of the documentation. It could also mean using speech recognition to get a draft of the documentation then using medical transcription services for further editing.
