Noise-robust open-set speaker recognition using noise-dependent Gaussian mixture classifier

Speaker recognition makes a decision to either accept or reject a recognized speaker candidate, based on some score (e.g. likelihood) associated to the item. Model-based classification can be used to make the decision. In mobile device applications, the background noise level may affect the score distributions and cause a decision failure. We describe a new decision procedure, which treats the scores as the outcome of Gaussian mixture distributions, where mean and covariance parameters are modeled as polynomial functions of noise level. We evaluate the procedure on a speaker recognition task in a mobile and noisy environment, using a hands-free microphone. Experiments show that the system delivers an equal error rate of 0.30%, 0.80% and 3.53% for parked, stop-and-go and highway driving conditions. The method maintains a balance between false acceptance and false rejection under all driving conditions, making any empirical threshold adjustment unnecessary.