Have you noticed spelling errors, lack of space between two consecutive words, or omitted words from the dialogues while watching some of your favorite shows on Netflix?

This is a fine reason why AI-powered transcription cannot replace human-powered transcription.


Artificial Intelligent (AI) and Machine Learning (ML) are empowering a variety of business processes nowadays. Organizations are switching towards AI & ML for increasing their productivity. The transcription industry is among the industries that witnessed the huge impact of AI. Almost every tech giant employed their best hands in developing some kind of automated tool for transcription or to make a speech-to-text converter.

Google, Amazon, and IBM are among the companies that are constantly working towards making their voice recognition better and impressive while recording and converting audio into text. Their transcription applications are the following:

  1. Google (Speech To Text),
  2. Amazon (Amazon Transcribe), and
  3. IBM (Watson’s Speech To Text)

Several transcription companies also claim to use AI-powered transcription processes to convert different kinds of audio into text and promise to revolutionize the way transcription is being done.

But no matter what they claim, “human-powered transcription still tops in terms of quality.”


AI-powered transcription is done based on the understanding of the application used for transcribing files as well as their historical knowledge about the language and speech. However, in practical terms, all these applications struggle in performing their task, no matter how advanced the learning approach they integrate for the transcription process.

Some of the common reasons that make human-powered transcription better than AI-powered transcription are:

Understanding Of Background Noise

AI-powered transcription applications require clear audio to maintain the quality of the transcription. In case of any background noise (like music, wind in a moving car, traffic, or background conversations as in courtroom proceedings), they struggle.

This is a situation where a human transcriber can distinguish between background noise and actual voice to be transcribed and deliver better quality transcripts.

Since it’s practically impossible to get perfect conditions for recording audio all the time, so organizations must opt for human transcription.

Multiple Speakers In A Conversation

Speech-to-text conversion apps perform better when there is just one speaker in recorded audio. However, in the case of a conversation between multiple people, they are not able to separate the voices from different people properly.

A group conversation (like legal proceedings, focus groups, interviews, panel discussions, etc.) is completely different than a recording by a single person. Some of the common differences can be:

  • one speaker might be louder than the other,
  • one might interrupt the other one in between their statement, or
  • Sometimes more than one person might speak simultaneously to make their point

These issues directly impact the quality of transcription done by automated transcription apps powered by AI & ML.

Multiple Accents In A Conversation

Accents are very important for AI-based transcription. Not every machine-powered transcription app is built to understand different kinds of accents. The same language is being spoken in different accents. The British accent is very different than the American accent. Native Americans speak in a different way than Asians.

Voice recognition in AI-based apps is still not so smart to figure out various accents used across the world. Though, companies like Google, Apple, and Amazon were able to enhance this ability in Google Assistant, Siri, and Alexa respectively. Still, it’s a long way to go for AI-powered transcription apps to understand and convert different accents from voice to text.


Though the transcription industry is facing intense competition from AI-powered transcription apps. In the past few years, innovation in AI & ML helped enhance the quality of transcribed audio. Still, for quality intensive transcription jobs, AI has a long way to go. Even if the AI-powered apps deliver better quality, they must go through a manual quality check to make sure the final transcript is good to go. Therefore, humans are still more effective in delivering quality results in transcription compared to AI-based apps.