Machine Learning versus Deep learning
Informative > Useful Clarifications
Why is TalkingID using a Deep Learning Artificial Intelligence (AI) instead of a Machine Learning AI ?
Machine Learning
A large number of speaker verification solutions on the market are based on machine learning (ML). Machine learning is based on hard-coded rules and always performs iterations according to the same pattern. Machine learning uses algorithms that analyze data, learn from it, and then use the new information to make decisions. ML enables a wide range of automated tasks, from finding the next song in a playlist to recommending a new product to a customer.
Deep Learning
Deep Learning has additional capabilities compared to basic ML algorithms. It automatically learns meanings from data such as images, videos or text without introducing hard-coded rules or domain expertise. For example, to identify green apples on an assembly line using machine learning you would insert instructions such as "apples are round" and "apples are green" to help the machine learn. Deep Learning doesn't need such instructions; it can figure out the features on its own.
Both deep learning and machine learning are important to almost every industry. They can help companies make more accurate predictions about consumer behavior, patient needs, marketing gaps, an identification and more. Deep Learning is designed for continuous, automated analysis of data, it should be applied to specific tasks that require the highest level of accuracy.
So especially in speaker verification TalkingID learns to make decisions based on a wide variety of features, as well as emerging ones on its own improving each time. Also, the Deep Leanig AI analyzes the voiceprints with a sample accuracy up to 1,000 times higher than common non-AI solutions on the market and at a maximum of 4 different verification levels.
That's why SmartBiometrics calls it a "Multilevel Forensic Voice Verification Model based on Deep Learning AI Technology".
Well, that's why TalkingID for short.