Silentium Systems


Silentium forensic speech and audio analysis software is built using neural networks and machine learning. Based upon a solid scientific foundation of physics and math, Silentium applications employ deep learning to automate speaker recognition and device identification processes.

Neural Networks

Neural networks, also known as artificial neural networks, are computer systems inspired by the human brain structure. They consist of layers upon layers of artificial neurons and the connections between them. Neural networks have become more and more powerful with the advancement of the hardware.

Neural networks are used to reproduce the way a human brain solves problems. Many of modern technologies rely on neural networks, such as automatic speech recognition, image identification, medical diagnoses, and recommendation systems.

Deep Learning

Deep learning is a subset of machine learning methods based on artificial neural networks. The word “deep” refers to the number of layers in the neural network. Deep learning algorithms can be trained to recognize patterns in the raw data.

Many of the tasks that heavily relied on the human workforce in the past now can be either optimized or fully automated. Forensic speech recognition is one of such fields. Silentium applications use neural networks and deep learning to automate and quantify speaker identification and device verification processes.

AI, Machine learning, deep learning neural networks

AI or artificial intelligence refers to the devices that replicate human cognitive functions, learning and problem solving being one of the most recognized. These areas are covered by the machine learning algorithms.

Deep learning neural networks are a powerful and precise tool that brings automation to many time consuming and complicated tasks. Top applications of deep learning across industries include visual recognition, fraud detection, pixel restoration, and automatic machine translation.