Problems With Automated Transcription and How to Solve Them

To be clear, we love automated transcription. We’ve built our entire company and software around it.

However, that experience has created an understanding of the problems with automated transcription. We wanted to share some of those lessons today.

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Why Should You Choose Automated Transcription?

Automated transcription is powerful and it is mindblowing how far speech recognition has come in a short time. Automated transcription is quick and cheaper.

Individuals and organizations use automated transcription when they need the results right away, have a low budget, and do not require a completely accurate transcript.

Individuals and organizations will also use this if they have the capacity or a tool to edit the transcript themselves.

Problems With Automated Transcription

In this post, I want to share some of the problems with automated transcription you should consider if converting audio and video to text.

We also share how our team at Speak AI is doing our absolute best for you to overcome those challenges in an intuitive, efficient, and cost-effective way.

Limited Vocabulary

Most systems come out with out-of-the-box speech recognition systems that are trained in general language and don’t include industry-specific, unique terms that are used in many individuals’ and organizations’ day-to-day lives.

Accents

We talk about bias in technology. Many of the people building speech recognition systems that are widely adopted are from primarily English-speaking North American companies. This creates a noteworthy bias in the speech recognition systems that has a big impact on the accuracy of automated transcription of speakers with accents and other languages.

Fast Talkers

I am guilty of this myself. Some people just talk fast. It is hard for a machine to process. Humans can review the transcript several times over to comprehend what someone says but as of today, machines do not do a very good job on this.

Low Audio Quality

Low audio quality can come from low-quality microphones. People record things while walking. They move around the room while speaking.

Background Noise

This connects with low audio quality. If you have ambient noise, cars driving by, bangs, booms, beeps, music or whatever, you will notice a dramatic drop in transcription accuracy.

Systems are getting better at filtering out noise but there is still quite a long way to go.

Overlapping Dialogue

It’s hard to realize before you review hours and hours of people having conversations, but you quickly come to understand that people love to talk over each other.

Our ears are amazing at decoding this and focusing on what we need to, but machines are not.

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All of this reduces the accuracy of the automated transcription. With that, you lose the professionalism of your final transcript. This is important if you are an organization sharing online. Additionally, if you are doing data analysis with automated transcripts, it can create false positives and creates a significant risk.

Transcription accuracy rates help you understand the percentage of error a transcript can have per word count. For example, a transcription accuracy of 97% means there is a 3% chance of errors per every 1,000 words or about 30 errors.

One error for an individual or organization can be damaging.

Most serious and academic researchers will not rely on automatic transcription. They know how delicate and complex language is.

Depending on what you are doing, automatic transcriptions can include potentially grave (and often tragically embarrassing) errors. One of my favourites was seeing egotistical changed to “eagle’s testicles”.

How We Are Solving The Problems With Automated Transcription

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We have made an intuitive transcript editor to help individuals and organizations edit the automated transcript.

Export To txt and Word Docs

You can also use Speak AI to export to TXT files and Word Docs so you can clean up any inaccuracies. You lose the clickable timing and navigation within Speak (we recommend you use the transcript editor to clean up and then export) but it is still a great way to improve your transcript accuracy.

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One of the big problems we see in automated speech recognition is speaker identification. Our system has high-quality speaker recognition. There are still some problems with accuracy but it gives a strong baseline to start with.

Soon, when you make one change, this will reflect through the entire transcript.

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This isn’t live today but is coming and will enable you to put your own vocabulary in the system. This will increase accuracy significantly.

Automatic Model Training

When you make an edit on the transcript, we will start to improve the accuracy of transcripts moving forward. This means every time you make a chance (or a human does through our Speak AI human transcription offering) you increase the accuracy for the next media file you upload.

Human Transcription

At Speak AI, we have augmented the automated transcription process with real people. We do an original pass of the audio and video with automated transcription.

If you want, you can edit up the transcription yourself. We’ve built an intuitive transcript editor that enables you to quickly find errors and correct them.

However, not everyone wants to do this. So, we’ve also built an incredibly powerful system to request people on our team to come and clean up the transcription to as close to 100% as pun-intended humanly possible.

Please check this site out to learn more and sign up today for free. Whether it is automated transcription or human transcription, we would love to help you out.

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What are the biggest problems with automated transcription?

The main problems are limited vocabulary, accents, fast talkers, low audio quality, background noise and overlapping dialogue. Each one lowers accuracy, which matters most when transcripts are shared or used for analysis.

Why is automated transcription inaccurate with accents?

Many speech recognition systems are trained mostly on North American English, so they handle other accents and languages less well. Clear audio, custom vocabulary and a quick edit of the transcript help close the gap.

How can I improve the accuracy of automated transcription?

Record with a good microphone, reduce background noise and ask people not to talk over each other. Then review the transcript in an editor, add custom vocabulary for specialist terms and correct speaker labels.

What does 97% transcription accuracy actually mean?

It means about 3 errors per 100 words, or roughly 30 per 1,000 words. That can be enough to mislead analysis or look unprofessional, so important transcripts should always be reviewed before they are shared.

When should I choose human transcription instead?

Choose it when accuracy matters most, such as research, legal or published material. Speak AI also lets you request a team clean-up of an automated transcript for those cases.

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Pros and Cons of Automated Transcripts

Why Choose 100% Human Transcription Over Automated Transcription

When Artificial Intelligence Can Transcribe Everything – The Atlantic

AI for Voice Transcription – Comparing Upcoming Startups and Established Players | Emerj

Automated Transcription to Analyze Your Data | NVivo

We tried audio transcription software Trint. Here’s what we found. – Berkeley Advanced Media Institute

AI vs Human Transcription Accuracy for Speech-to-Text Services – Rev

Manual or Automated Transcription