Phonexia introduces DNN-powered voice biometrics engine
27 March 2018 13:31 GMT

Biometrics firm Phonexia has just launched Deep Embeddings – the latest generation of its voice biometrics engine for speaker identification and verification.

The new technology exclusively uses deep neural networks (DNN) to map voices directly to their unique small and fixed length records called voice-prints.

In a statement, the firm said Deep Embeddings – available within the Phonexia Speech Platform – is the world’s first commercially available voice biometric engine with this machine learning capability.

Phonexia Deep Embeddings  uses a discriminative training model to identify the truly unique features in each individual’s voice. As a result of incorporating these new training models in its DNN, the new Deep Embeddings  technology is able to create voiceprints twice as fast, have an accuracy that is 2.4 times greater, and have a memory consumption which is just a quarter of the previous Phonexia voice biometric engine – which was already one of the fastest and most accurate on the market.

“The technical benefits –accuracy, speed, and reduced memory use – from transitioning completely to deep neural networks in our engine have exceeded our expectations,” stated Petr Schwarz, Phonexia CTO. “We are looking forward to our clients seizing these benefits as they implement our technology in their systems.”

The firm said Deep Embeddings has had a significant improvement in its accuracy as measured by the Equal Error Rate – the combination of False Accept and False Reject scores. Deep Embeddings reduced these scores by 2.4 times in comparison to the previous voice biometric engine.

“At the end of the day, higher accuracy saves money -- whether this is decreasing the probability of a client having a false rejection during a call center’s phone verification or increasing the accurate identification of fraudsters misusing someone’s identity to take out a bank loan,” said Pavel Matějka, Phonexia CSO.