Why Do We Still Need People For Audio Transcription In AI?

Automatic audio transcription costs a fraction of what manual transcription does. To improve the accuracy, automatic voice transcription will require the help of human transcribers. Audio transcription seems like a straightforward task. All you have to do is record the audio recording. However, audio transcription is a complex task that AI developers need to access Audio Dataset, OCR Datasets and many more datasets

ASR (automated audio recognition) can easily handle simple transcription tasks. ASR suffers in some cases, so clients often approach us for audio data collections and transcription.

It is impossible to find a single-size-fitssall solution for voice transcription when you consider the varied and often bizarre requirements of AI Training Dataset today. Consider the following aspects before beginning a transcribing work: your use case, budget, quality criterias, required language skills and more. We'll be discussing why humans are still required to transcribe in an increasingly automated setting. Also, why we prefer a consultative approach to AI transcription.

What is AI transcription?

There is a clear distinction between transcription for general purposes and transcription to aid artificial intelligence. Audio transcription to AI is a transcription which is used in combination with audio recordings in order to train and evaluate voice recognition algorithms across a range of applications. The transcriber (which can be a person of a computer) records the words and their meanings. Some transcriptions may contain nonverbal sounds and background sound. Transcribed audio for AI can include human-to–machine audio (e.g. vocal commands or wake-words) or human–to-human audio. Interviews or phone conversations.

AI transcription is different from voice transcription. AI transcription can be used to create podcasts, audio interviews, transcripts for court cases, television episodes and phone support calls. This is usually the end goal. The user is interested in being informed about what was said. The end-use scenario will determine the type of transcription used and what is translated. The following are the three types of audio transcribing:

• Transcription verbatim. A transcription of spoken speech word for word. It records everything that the speaker speaks, including fillers such "ah," uh," or "um," as it also includes throat clearing and incomplete phrases.
• Automatic verbatim transcribing: This allows you to extract meaning out of the information. The transcriptionist performs light editing to correct sentence structure.
• The transcription was edited: To ensure readability and clarity, the script must be formalized and modified.

Audio recognition technology uses verbatim transcription to help you understand all aspects of audio recording. Intelligent verbatim transcription could also be used when it is more crucial than mapping the audio input to words.

Why are human transcribers still needed for AI purposes?

Automated transcription solutions are more affordable and quicker than human transcription for your day-to-day transcription needs. However, for those cases when computerised voice recognition fails, human audio transcript is still needed. These are some examples.

To increase ASR accuracy in human-to–human communications

Recent research showed that the word-error rate (WER), used by ASR to transcribe business phone conversations, was still between 13.3 and 23.3%. This is significantly more than previously recorded error rates of 2-3%. ASR seems to handle chatbot-like interactions between humans (and machines) quite well. This is because individuals communicate clearly with machines while speaking to them less clearly with people.

ASR error rates in excess of two-digits can have major implications for high-stakes industries, such as law, health which requires Medical Datasets, or autonomous vehicle manufacturing. ASR developers want to continue employing human transcribers even when transcription is failing.

To deal effectively with difficult environments and use-cases

ASR, aside from accent recognition is meant to deal with increasingly complex auditory environments as well as conversational contexts. ASR was originally meant to work in a tranquil bedroom or home. Today, however, it is expected to function in noisy environments, automobiles and parties.

Audio captured in a quiet area can be difficult to transcribe if there is background music, low audio quality or many competing speakers. ASR might still have difficulties in these situations, so human transcribers will be better equipped to handle them.

Audio Datasets (and GTS)

There are many aspects to consider when optimizing your audio datasets transcribing for AI. It is important to find a provider of Speech Datasets who is flexible, adaptable, and committed to your best interests. If they aren’t interested in your end use case and offering multiple solutions, they might not be the best match.

Global Technology Solutions offers data solutions professionals who work together with clients to determine the transcribing requirements. And, if your requirements have not been fully established, we may be able help you select the best solution. We are available to assist you with GTS transcribing.