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Future of AI in Transcription,  Human vs. AI Transcription

6 Common AI Transcription Errors and How to Fix Them

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The use of AI in the transcription industry has become so common.

However, the common AI is becoming, the more common the transcription industries are facing AI transcription errors.

With these current AI transcription technologies, the harm done to the transcribed data cannot be left unsaid.

Transcribers and transcription companies face many AI challenges when using this technological approach.

Among many, safety failures and exposing your client’s data to unauthorized persons are just some of the AI challenges.

Still, out of reach, AI is the product of human intelligence. However, this technological shift has its adverse transcript outcomes.

The article below analyses what AI transcription errors mean and the six common AI transcription mistakes while offering solutions for fixing them.

Where do they occur, and why do they occur? This informative piece seeks to address these questions, providing clear information and guidelines for their solutions.

But before delving into the six common AI errors. Let's learn how AI transcription errors come forth and what they are all about.

What’s AI Transcription Error?

First, let’s understand what AI transcription means to clearly decipher the meaning of AI transcription errors.

AI transcription refers to computer techniques for transcribing spoken data, whether for recorded audio or videos.

The systems are fitted with data learning algorithms that make it possible to recognize, absorb and transcribe the recorded information.

Some examples of these data algorithm techniques include machine learning, deep learning AI systems, Google API and many others.

Having understood this, AI transcription errors are the data inconsistencies from computer-generated texts.

They are the common mistakes that AI transcribing software results in when the spoken data is converted into text formats.

How do these AI transcription errors occur? How can one reduce these common AI errors when transcribing data?

Depending on how these tech tools absorb and discern content, there are four types of AI transcript errors. The AI transcription errors depends on the following:

AI generating written texts against the familiar rules of writing

AI failing to perceive conversation events as they have been recorded

Difficulty of AI failing to showcase grammar rules in its written content like grammar structures, syntax arrangement, word phrases, pauses and others.

AI failing to guarantee data safety and confidentiality

Regarding the above, no matter what kind of service you provide in the transcription industry. Whether academic, media, legal or medical transcriptions, you will encounter these common AI transcription problems.

The Six Common AI Transcription Problems and Solutions

1) Bias and Inaccurate Transcripts Results

Surprisingly, AI algorithms are designed to prioritize certain data features or patterns over others. This innovative method results to outdated or incomplete data results.

As a result, neglecting important data information. Sometimes, AI generates texts that fail to correspond to the original meaning of the oral recorded content.

How to solve this:

Double-check the transcript results to maintain the original meaning of the recorded data.

Cross-reference your transcripts generated texts with the actual data context to ensure data validity.

Select an AI model that is suitable to handle specific data domains to enhance transcripts accuracy.

2) Data Accents and Dialects Misinterpretation

AI fails to diversify conversations as humans do. It fails to interpret several linguistic features, especially for multilingual speakers.

The AI algorithms cannot comprehend various accents and dialects. It's especially true when language idioms, nuances, and slang are showcased in the oral data.

How to solve this:

AI developers must innovate software that detects all languages, accents and dialects.

Human transcribers, too, should come along to verify and scrutinize the AI-generated texts to correctly revise the misinterpreted data.  

3) Ethical Considerations for Transcripts

AI-transcribed data may appear similar to other forms of transcribed data. AI tools generate content depending on the kind of data the tool has been exposed to over time.

Due to this, the AI tool may plagiarise content from other software that may have experienced an earlier exposure to similar data being transcribed.

This kind of feature necessitates shaken working principles that deterioriates the transcription process.

How to solve this:

Transcription companies need first to assess and examine the transcription tool.

They also need to clear any data that might appear similar to their tech software before beginning the AI transcription process for all projects.

Ensure the company's guiding principles are well followed to enhance the professionalism of your transcription services.

Test the AI tool way before you start transcribing any project to see the kind of data generated.

4) Limited Oral Data Features

AI tools lack proper comprehension of the spoken content. Language features such as punctuation, intonations, gestures and other emotional aspects are crucial in recorded conversations.

These features maintain the naturalness of the oral data.

However, AI tools cannot produce these features like in their spoken form. It fails to go beyond the basic conversational components.

How to solve this:

Review the data features on the generated AI transcripts and include them where necessary via human touch.

Implement AI-trained models capable of understanding and showcasing language gestures, nuances, and emotions.

Practise data curation to focus on diverse oral features and scenarios presented on the AI transcripts.  

5) Unsecure AI Transcripts Files

AI-transcribed data does not guarantee safety. Online data exposure may lead to data access by unauthorized personnel.

Technological hitch-ups such as data leakage and copyright issues exist in today's tech era. You can also get scammed, or your data may get misused by anonymous people.

How to solve this:

If transcription companies are likely to use AI to transcribe content, thoroughly vetting their team is crucial to creating a professional atmosphere.

The team can sign NDA(s) and be taught how to handle client data. Also, the companies should integrate strong logins like passwords, three authentication entry steps and others.

Implementing these strong entry logins prevents data access from unknown people and keeps the AI-transcribed data for the clients as safe as possible. 

6) Wrongly Identified Conversation Speakers

AI struggles to identify conversation speakers, especially if they share the same name or if the speakers are many in number.

If that’s the case, AI may wrongly identify the speakers, leading to the jumbling of spoken content without proper identification of whomever spoke the right data.

How to address this:

Integrate AI models with the capability of identifying incorrect speakers as well as selecting the right one.

Manually edit the transcript and label each speaker with their spoken oral content.

Conclusion

The existing advantages the transcription industries gain from AI are thrilling.

For instance, transcribing data quicker allows the delivery of transcripts faster and enhances the writing quality of the transcripts.

Therefore, it’s difficult to outdo this innovative transcription approach; however, the flaws.

Discovering these errors and their solutions exposes various findings that AI transcription errors can be looked upon and resolved.

These findings can be a reference and a guide for transcription companies to select suitable AI tools for every transcription stage.

Furthermore, the findings about AI transcription errors such as biased and inaccurate transcript results, limited oral data features and others. It can be helpful in future research.

These findings can also be helpful to transcription companies by offering practical strategies on which AI can be leveraged to provide effective transcript results that are of standard and quality, as Verbalscripts transcription services do.

Verbalscripts transcription team analyses the transcribed data correcting the AI transcription errors, later releasing accurate final transcripts.

The Verbalscripts team locates the data inconsistencies and pinpoints the sections to be revised through human touch.

If this is what happens at Verbalscripts transcription company, why not seek transcription services from them? You will be delighted by the transcript's results, which are of a high mark.