Technology keeps taking over manual work that previously took human’s energy and time to complete. Humans are willing to give out the tedious tasks to machines and consequently save on overhead costs, training time and get more job done in less time.
In machine transcription, that has not been the case. There are all sorts of speech recognition software in the market, but none matches the quality of manual transcription. The main reasons to acquire an artificial intelligent voice recognition software is to increase productivity by having more work done in half the time.
Unfortunately, as you’ll discover in this article, machine transcription is more of a liability than a helper.
7 Reasons Why human Transcribers are Still the Best Option
1. Audio Quality
Machine transcription converts spoken word into the written word. They cannot differentiate a noisy background from the actual speech that needs transcription. To use machine transcription you have to be in a reasonably quiet environment. If you’re in a noisy place, the machine will transcribe the background noise as well, and you’ll end having a document that makes no sense.
On the other hand, human transcribers can differentiate your speech from the background noise. Regardless of the quality of your audio, you’ll get a high-quality document that express your message as intended.
2. Multiple Speakers
Conversations among numerous speakers can quickly overwhelm a transcription robot. It gets worse if the speakers have different accents in a noisy place. The device is not smart enough to recognize the exchange of information among people, and consequently, it transcribes as one prose.
Professional human transcribers can tell a when there are multiple speakers. Their intelligence can sense different accents, voice tones and thus decipher as such.
3. Context & Meaning
Humans are versatile, they have experience and training on the subjects, and when they’re transcribing, their knowledge, experience, and emotional intelligence help them create correctly reading documents.
Machines transcribe; they do not understand the context or the deeper meaning of an audio file. In non-verbatim files, the machines are ineffective because they can’t fill out additional words other than what is in the audio file.
4. People and Place Names
A machine gives out words as they sound depending on the dialect used. Since they do not understand the meaning, they may spell out homonyms that mean something different from the context.
Humans follow the conversation and understand the meaning of the words spoken. As a result, they spell words to fit the setting even when several words could sound the same.
5. Accents
Different dialects have different accents. For a robot to transcribe correctly, it needs training on all the accents which can take up too much time. When accents affect the pronunciation of words, the machine may spell them out to the closest resembling word available in their system; and can ruin the meaning of the whole document.
Humans come up with the accents thus they can understand them. Different accents have different spellings for various words, and only a human can get this immediately and work accordingly.
6. Dialects & Slang
When a machine receives an unfamiliar word, it immediately autocorrects it to the closest meaning or skips the word altogether with the reason that it’s inaudible. Different societies have different words in slang that robots cannot understand unless their programming changes. Humans can immediately tell a slang by researching the word and spelling it out correctly to fit the document.
7. Punctuation & Grammar
Most machines do a good job on punctuations, but none can beat the perfection of a human transcriber. The machine may interpret a pause to mean the end of a sentence when in essence, the speaker may be trying to come up with the next part of the same sentence or sipping a drink.
Conclusion
It only takes a human to understand another human. The machine can learn, but they cannot get to the perfection of a human transcriber. A human can get a message through a facial expression or a body language while a machine may interpret that as silence. For organizations that value quality over quantity, human transcription is the route to take.
