Stochastic feature compensation methods for speaker verification in noisy environments
Stochastic feature compensation methods are explored for robust speaker verification. Front-end denoising using Gaussian Mixture Models (GMMs) built from stereo training data. Feature compensation based on joint probability models (GMMs) of noisy and clean features outperform individual model based compensation. Feature compensation using GMM-based cepstral trajectory mapping shows the overall best performance.
speaker verification noisy environment gaussian mixture models minimum mean squared error maximum likelihood estimate expectation maximization algorithm