Why do we still need people for Audio transcription in AI?

 


INTRODUCTION

At a fraction of the expense and effort, automatic audio transcription has achieved near-human accuracy levels. However, if you want to improve the accuracy of automatic voice recognition, you'll still need the assistance of real-life human transcribers. On the surface, audio transcription appears to be a simple task: write down what was said in an audio recording. However, as a data source for AI developers, the transcription projects on our plate today are far from straightforward.

 

This is because automated audio recognition (ASR) already handles simple transcription scenarios. When clients approach us for audio data collection and transcription, they are looking for solutions to the edge cases where ASR still suffers, such as detecting a wider range of accents or dealing with background noise.

 

When considering the unique (and sometimes bizarre)  AI training dataset requirements of today's audio technology developers, a one-size-fits-all strategy for voice transcription is doomed to fail. Before beginning a transcribing project, consider the following project factors: use case, budget, quality criteria, required language skills, and more. In this essay, we'll look at why human transcription is still needed in an increasingly automated environment, as well as why we adopt a consultative approach to transcribing for AI.

 

What exactly is AI audio transcription?

There is a distinction to be made between general-purpose transcription and transcription for artificial intelligence. Audio transcription for AI, in particular, is a transcription that is used in conjunction with audio recordings to train and evaluate voice recognition algorithms on a wide range of applications, including voice assistants and customer support bots. The transcriber, who can be either a person or a computer, notes what is said when it is stated, and who says it. Nonverbal sounds and background noises may be included in some transcriptions. Transcribed audio data for AI can be human-to-machine audio (e.g., voice commands or wake words) or human-to-human audio (e.g. interviews or phone conversations).

 

Transcription for AI differs from conventional voice transcription, which is used for everything from podcasts to office meetings, interviews, doctor's appointments, court procedures, TV episodes, and customer support phone calls. The transcription itself is usually the end goal in this scenario. The user wants to be informed of what was said. The type of transcription employed and what is transcribed is entirely dependent on the end-use case. The three major types of audio transcription are as follows:

 

Transcription verbatim: A transcription of spoken language word for word. It records everything the speaker says, including fillers such as "ah," "uh," and "um," as well as throat clearing and incomplete phrases.

 

Automatic verbatim transcription: To extract meaning from what was stated, a layer of filtering is applied to the transcription process. The transcriptionist does light editing to correct sentence structure and grammar, as well as removing unnecessary words or phrases.

 

The transcription has been edited: For readability and clarity, a complete and exact script is formalized and modified.

 

To catch up on all of the intricacies of the audio recording, most audio recognition technology requires verbatim transcription. Intelligent verbatim transcription can also be employed when the overall meaning of the audio segment is more essential than mapping the auditory input to words.

 

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Why do human transcriptionists continue to be required for AI?

While automated transcription solutions are less expensive and faster for day-to-day transcribing needs, human audio transcription is still required for use cases when computerized audio recognition fails. Here are some relevant examples.

 

To increase the accuracy of ASR for human-to-human communications

Recent research discovered that the word error rate (WER) for ASR used to transcribe business phone conversations were still between 13 and 23 per cent — significantly higher than previously documented error rates of 2-3 per cent. According to the study, ASR handles "chatbot-like" interactions between humans and machines rather well since individuals speak more clearly when speaking to a machine but not nearly as clear when speaking to a person.

 

Double-digit ASR error rates could have major ramifications in high-stakes industries such as health, law, or autonomous vehicles. As a result, ASR developers are still eager to employ human transcribers in circumstances where transcription is failing.

 

To deal with complicated environments and use cases

Aside from accent recognition, ASR is intended to handle increasingly complex auditory environments and conversational contexts. ASR was originally designed to function in a peaceful bedroom or home office, but it is today expected to work in crowded workplaces, automobiles, or parties.

 

When there is background noise, low audio quality, or several competing speakers, transcribing audio captured in a quiet room can be difficult. Human transcribers are better prepared to deal with these difficult audio settings in which ASR may still struggle.

 

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Audio Datasets and GTS

When it comes to maximizing your needs for cost and delivery speed for audio datasets transcription for AI, there are numerous elements to consider. When considering audio data transcription service providers, look for one who is adaptable, flexible, and concerned with your best interests. They're probably not the greatest fit if they're not delving deeply into your ultimate use case and presenting several solutions.

 

At Global Technology Solutions, our data solutions professionals collaborate with you to determine the amount of transcribing you require. And if your requirements aren't yet fully defined, we may assist you in selecting the best solution we provides you text dataset, image dataset, speech dataset, video dataset along with annotation. Contact us right away to get started with GTS transcribing.

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