Voice Recognition

Features such as the length of the vocal tract, nasal passage, pitch, accent, and even behavioral characteristics of each individual, are used to create an algorithmic model known, as a voiceprint. Voiceprints, similar to fingerprints, utilize specific vocal characteristics of a particular person instead of a mark left by the friction ridges of a human finger to create a unique algorithmic model of the voice. Voiceprints are commonly used for biometric identification and authentification processes.

Although various machine-learning techniques are used to process and store voiceprints, current models are based on deep learning, which has shown to outperform other models on a broad range of tasks involving noisy sensory data. Models based on deep-learning are able to reconstruct a speaker's face by merely analyzing a voice recording and using a technique called deep clustering, which disentangles multiple conversations happening at the same time in a crowded room.