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February 13, 2020

Misram is Using Machine Learning for Voice Authentication

AI Cybersecurity 2019 Patent Forecast®

Misram LLC's latest published patent application uses machine learning (ML) to determine the probability of spoofing a voice.

With voice authentication becoming more and more common, the probability that a voice has been spoofed, using a synthesized voice or a converted voice, is increasing. As such, many conventional voice-based authentication systems remain highly susceptible to spoofing.

Misram is using a ML, multi-dimensional acoustic feature vector authentication system to build and train multiple multi-dimension acoustic feature vector ML classifiers in order to determine the probability of a voice being spoofed. Their system extracts a number of acoustic features from a user's voice sample and converts those features into a multi-dimensional acoustic feature vector. Ultimately, the spoofing probability indication is used to determine whether or not to authenticate a user.

This is Misram's third published document in the AI Cybersecurity sector, classified under the Biometric subcategory.

Podcast, Microphone, Wave, Audio, Sound, Recording

Relevant Patent Documents

Application 20200035247  


AI Cybersecurity 2019   Patent Forecast®

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Cyberattacks impose considerable costs on the economy. Virtually every industry in this information economy, such as aerospace and defense, healthcare, retail, telecom, entertainment, manufacturing, banking, financial services and insurance (BFSI), is concerned with their Cybersecurity. According to a recent Gartner forecast, worldwide spending on information security products and services is expected to grow to $93 billion in 2018. The global Cybersecurity market is projected to reach a size of $165.2 billion by 2023, growing at a CAGR of 10.7%.