Written by Zaheer Carrim, Pieter van der Merwe and Ridha Moosa for izwe.ai
In the quest of quality and accuracy of the best Machine Learning (ML) model; data validation has become the most important aspect of the process. Mainly to catch errors, differentiate models, and have the ability to produce quality results when met with data drifts.
Artificial Intelligence (AI) models without clean data sets may cause your model to not perform as well in a real world setting as it might have biases and errors.
Clean Data Sets in transcription
Data sets in transcription are streams of audio with different variables. These variables can include language, tone, accent, pronunciation and slang or localised language terms.
When faced with these data sets, human intervention can drastically improve outputs and quality.
Ideally, the machine must be able to infer differences on an almost human intuitive level. Variations such as accent, tone, language, and pronunciation can all influence models and need to be considered for a more robust model.
The AI models understanding, and processing of data is dependent on feedback and data within parameters set.
To fill the void of this uncertainty; supervising a machine learning model allows for the data sets to be transcribed accurately. To give confidence in the data being used; it is integral to use human agents to give repeated feedback, which allows the machine to learn and discern differences.
In transcription, we want to ensure the system is able to output higher confidence data. For example, to increase confidence scores, human agents can correct the words or errors from a data stream (video or audio file) that may occur during the automation process. This “feedback” from the human allows the machine to progressively improve its view and processing accuracy.
This could include colloquial and nuanced speech, slang, and buzzwords or lingo used in a particular industry.
The effectiveness of the AI to transcribe words from a given data stream can also improve by using code to rate the word level confidence of each word.
Furthermore, the use of sub-word tokenisation algorithms helps to group frequently used words and language terms without breaking them apart. This system also groups rare words and their extended forms. E.g., colourful/ ly (colourfully-adverb).
What are the accuracy and speed rates when using a human agent?
In transcription, an unsupervised audio stream goes from 70% accuracy to over 95% with a human in the loop. Due to the human ability to spot differences, errors, nuance and tone. As the data is captured, and edited, the machine begins to learn to accurately predict the speech when faced with the same instances.
Crowdsourcing- and why it hurts Machine Learning.
Transcription and annotation services have seen many entities offer competitive prices though crowdsourcing -or offering the task to a large pool of people. This opens the door to inconsistency. Mainly because agents’ skill and aptitude for feedback data isn’t guaranteed and having the same agents stay for the entire process becomes challenging. When a project is disrupted, it affects the data sets that are being used to train machines. Agents who stay for the entire process and are vested in a project are able to improve their entry data, make corrections and learn from previous mistakes.
Transcription and annotation services have seen many entities offer competitive prices though crowdsourcing -or offering the task to a large pool of people. This opens the door to inconsistency. Mainly because agents’ skill and aptitude for feedback data isn’t guaranteed and having the same agents stay for the entire process becomes challenging. When a project is disrupted, it affects the data sets that are being used to train machines. Agents who stay for the entire process and are vested in a project are able to improve their entry data, make corrections and learn from previous mistakes.
Getting the right ML Partner
The ability to expedite voice to text, has many benefits for any industry or business. From voice driven interactions, health and wellness exchanges to customer relationship management. Building an AI that transcribes and identifies a large amount of variables can aid and enhance the company’s positioning.
At izwe.ai, training data is administered by transcribers with localised languages. This allows the algorithm to maximise the parameters and size of the training data at scale. A dedicated team of transcribers who speak multiple languages, and are trained linguists, allows data sets to have a continuous feedback loop whilst growing the size of the datasets. This feedback loop means human agents and machines are in a seamless rotation with clean data sets. A team that is tailored to your business and is well acquainted with validating and annotating your data. This scale- on- demand team allows easy clean-up of pre-annotated data, structuring data according to your customised business needs. It also allows you to expand your data sets with the same agents that are already accustomed to that particular context from inception.
Comparative Reporting
Reports are crucial to understanding what is happening and why it’s happening.
Data charts are visual tools that help to discover trends, identify patterns and decipher data that is both complex and intricate. With just the right number of data and variables, you are able to process information quicker and easier.
A mental model of the human-in-the-loop process for predicting labels on data
“Sampling the right data to label, using that data to train a model, and using that model to sample more data to annotate.” livebook.manning.com/book/human-in-the-loop-machine-learning
The platform that works with humans-in-the-loop seamlessly
Editing tools that maximise productivity and are built to work on pre-annotated data and new labeling of data sets with ease. Agents become familiar with data sets quicker and are able to clean-up and adjust within frames to meet quality requirements.
The Enlabeler Platform
izwe.ai Offers transcriptions with annotations. Our agents are highly skilled linguists and data stream interpreters. Experts who are grounded in their link with machine learning that labels and validates data seamlessly. Speed and accuracy is enhanced with a machine learning platform that also provides constant model performance for both validated and unvalidated data.
Enlabeler — izwe.ai
Enlabeler provides high-quality human-in-the-loop validation
Our in-house agents are highly skilled in language processing and transcribing data streams to heighten machine learning. Our annotation and transcription team are dedicated and ready to efficiently label your data.
Learn more about how we can assist you by reaching us at hello@izwe.
