Training "on the fly" to improve the performance of speaker recognition in noisy environments
Ahmed H. Y. Al-Noori, PJ Duncan, FF Li · University of Salford Institutional Repository (University of Salford) · 2017
Reliability of Speaker Recognition (SR) is crucial for critical applications, especially in adverse acoustic conditions. Ambient noises and their variations represent a significant challenge for such applications. In this paper, a new technique is proposed to address the issue of performance degradation in noisy environments. Based on the estimation of the signal to noise ratio (SNR) and profile of the ambient noise from input signals, the proposed method re-trains the enrolment model for the claim speaker to generate new noisy models that adapt to the noise profile. This technique is termed “training on the fly”. Evaluation results show notable enhancement in performance in terms of the reduction of equal error rates over a range of SNRs and different types of noise.