Voice command recognition for noisy environments by means of cross-correlation portraits

Abstract

Methods of voice command (VC) recognition in heavy noise environments are required for precise work of speech information systems on the factory floor and in transport. The paper considers a speaker-dependent way of VC recognition for VCs belonging to a limited vocabulary and being recognized in heavy noise environments. For this purpose, VCs are transformed into cross-correlation portraits (CCPs), i.e. special images. The VC under recognition is referred to a class with a minimal distance (metric) between CCP of this command and model CCPs of the class. The authors elaborated algorithms for VC transformation into CCPs, a method for defining VC boundaries, ways of model command optimization and metric choice. As a result, a rather precise VC recognition in heavy noise environment was obtained.