An Improved Ranking-Based Feature Enhancement Approach for Robust Speaker Recognition

Although the field of automatic speaker or speech recognition has been extensively studied over the past decades, the lack of robustness has remained a major challenge. Feature warping is a promising approach and its effectiveness significantly depends on the relative positions of each of the features in a sliding window. However, the relative positions are changed due to the non-linear effect of noise. Aiming at the problem, this paper takes the advantage of ranking feature, which is obtained directly by sorting a feature sequence in descending order, to propose a method. It first labels the central frame in a sliding window as speech or noise dominant ("reliable'' or "unreliable''). In the unreliable case, the ranking of the central frame is estimated. Subsequently, the estimated ranking is mapped to a warped feature using a desired target distribution for recognition experiments. Through the theoretical analysis and experimental results, it is found that autocorrelation of a ranking sequence is larger than that of the corresponding feature sequence. What is more, rank correlation is not easily influenced by abnormal data or data that are highly variable. Thus, this paper deals with a ranking sequence rather than a feature sequence. The proposed feature enhancement approach is evaluated in an open-set speaker recognition system. The experimental results show that it outperforms missing data method based on linear interpolation and feature warping in terms of recognition performance in all noise conditions. Furthermore, the method proposed here is a feature-based method, which may be combined with other technologies, such as model-based, scores-based, to enhance the robustness of speaker or speech recognition system.