When companies and other organisations need multilingual transcriptions of spoken content, which could range from podcasts to videogames, they need to find providers that can access a pool of talented professional translators who are also fast typists and are confident users of Artificial Intelligence and other computer-assisted tools.

Article in partnership with Day Translations. Because so much online content is increasingly comprising spoken word, automatic captions have become very common in videos, either in short or long form. Companies and any organisation with an international strategy may want to reach and communicate with wider audiences by making their spoken word products available in multiple languages. That’s where language transcription can play a vital part in this process. While automation plays a part in this landscape, the majority of the work needs to be done by humans, as there are many factors to consider in each project, primarily when it comes to issues of confidentiality, ambiguous meanings in different languages and sensitive information that requires discretion.

Is Language Transcription Completely Automatic?

While auto-captions can cover many languages and can be produced quickly at the touch of a button, the industry still needs professional translators that can transcribe spoken scripts, for example phone calls and court hearings.

Looking at job sites and job vacancies on LinkedIn, language transcription is a sought-after skill that companies need in a variety of sectors including gaming, medical and legal.

Transcription companies pay freelancer translators between $15 and $20 and they may require that candidates can demonstrate to have excellent spelling and grammar, as well as specific industry knowledge such as being familiar with technical terms in their chosen language.

The work of freelance language transcribers inform Artificial Intelligence to incorporate the human perspective into data, looking at the human-machine interaction, finding patterns and integrating different languages. Transcribers writing in different languages can work either on dictated text or recorded live speeches.

European languages from French and German to Finnish and Polish are in demand, as well as Arabic in a variety of transcription jobs. It is important to note that some niche languages present more challenges for AI as they require more resources than popular languages such as German and Chinese that are covered extensively in research, but technology is catching up very quickly to offer better quality translations.

Language transcriptions are still mainly human-intensive, with translators either transcribing directly from spoken word or correcting and editing computer-rendered scripts. The value-added labour from human transcribers is the labelling and categorising of information, which requires interpretation of the context as well as factors such as intonation and pace. This process enriches machine data learning.

There is also the issue of inaccurate information of data rendered by AI, such as ChatGPT for example, which has a severe impact on how reliable translations can be of transcribed documents. This means that, especially for confidential documents containing sensitive information, such as medical reports, machine language transcriptions are not recommended and language professionals should be allocated to deal with these cases. Not only machine-rendered language transcriptions can contain errors but due to the nature of open access through cloud computing of AI-generated text, confidential information should never be shared on public platforms.

The Human Touch: Dealing with Ambiguous and Sensitive Content

From moderating forums to making training videos more accessible, transcribers that specialise in language translation need to be discerning with topics that may cause confusion or offense if they are translated badly in another tongue.

While a lot of work is being carried out to erase or at least limit inherent biases in AI-generated content, as well as deliberately stopping any incitement to hate and violence, there are many intangible elements that are unique to each language and the related culture that only humans have the necessary qualities to understand and convey nuance.

For example, the role of multilingual content moderators on YouTube is essential to ensure that the viewing experience of videos that are considered as being inoffensive in one country are not banned in other countries.

It takes a human to fully comprehend the human experience in all its facets; a machine can aim at mimicking and can use keyword search tools to spot any red flags, however things like tone of voice can hugely influence the real meaning of words, even the most innocuous, when used out of context.