A balance of automated and human assisted data collection gathers and interprets exactly what you need in the context of your innovation.
The linguist was at the core of translation until machine translation came long. As a result, language service provider began to shift from being language experts to being language AND technology experts.
Roles previously held by tech employee like data engineers and solutions architects are now commonplace at LSPs.
LSPs now have first-hand experience with the challenges of developing AI technology as well as the necessary technical experts in-house to support and develop these innovations the technology necessary for data solutions.
What we’ve learned over the years is you need a balance of automated and human assisted data collection. The tools are out there to gather large swaths of data, but humans still rule with respect to interpreting exactly what you need in the context of your innovation.
What you need is specialized, human-assisted data collection and not an all-encompassing, quick solution. This saves you money in the long run and get you exactly what you need.
Human-Made Text Data for NLP Systems
About 10 years ago, it became apparent that machine learning systems learn better from clean data.
Our team of in-country subject matter experts can identify data worth collecting.
They can also assist an automated scraping process.
The human-in-the-loop method provides higher quality data than crowdsourcing and automated scraping.
Consider the recent ethical considerations that came to light regarding ChatGPT.
You want to keep costs down but get your innovation to market before the competition. And you want to do it well to boot.
So, you can automate your data collection and miss out on the important human touchpoints that ensure quality and accuracy. You can also outsource the collection and labeling, running data through the gamut without clear direction and fair compensation.
But, trying to do too much too soon can result in problems down the road.
The same teams can collect and annotate image data to help train your object recognition engines or language or domain dependent OCR systems.
We can call on the crowd for help capturing photo & video with their phone, and label or annotate the data upon submission.
Again, AI annotation is a way of the future.
However, humans must still check it to get the best possible and most accurate readings of your image data.
As a data solutions provider, it’s our role to highlight all the ways in which your dataset can be customized, while also steering you towards the most effective and price-conscious collection option for your solution.
Speech and Audio Data Collection
We collect multilingual audio and voice data for ASR (Automatic Speech Recognition) engines.
ASR systems not only need large quantities of high-quality data, but they also need the data to match the exact technical specification to help the systems to cope with noise, background sounds, multiple speakers, and slang.
We create custom audio data sets according to your technical requirements. We can also create speaker profile balanced datasets, scripted or non-scripted recordings in over 80 languages.
Human made or human reviewed transcription can complement these recordings as well.
Benefits of Human-Assisted Data Collection
So, let’s say your company is trying to solve for the edge cases where automated speech recognition still struggles – toxic language in video games, for example.
We initiate natural data collection and err on the side of human transcription. That’s to ensure accuracy and inclusivity and to handle complex environments and use cases.
Partner With Us for Human-Assisted Data Collection
As innovators in the data collection space, Summa Linguae Technologies offers flexible, customizable data services that evolve with your needs.
To see how we can help you with your data collection project, learn more about our data solutions here.
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