Multi-scale kernels for short utterance speaker recognition

Abstract

Short utterance is a great challenge for speaker recognition, for there is very limited data can be used for training and testing. To give a robust estimation, the amount of model parameters for the short utterance should be less than that for the long utterance; however, this may impede the models descriptive capability. In this paper, we propose a multi-scale kernel (MSK) approach to solve this problem. We construct a series of kernels with different scales, and combine them through multiple kernel learning (MKL) optimization. In this way, the robustness and scalability of the model will be both enhanced. The experimental results on NIST SRE 2010 10sec-10sec dataset show that the proposed MSK method outperforms the traditional Gaussian mixture model supervector (GSV) followed by support vector machine (SVM) method.

Speaker verification with short utterances: a review of challenges, trends and opportunities

“…The work in [79] proposed a multi‐scale kernel learning approach to address the problem in GMM‐SVM framework. It constructs a series of kernels with different scales and combine them through multiple kernel learning optimisation.…”

An overview of robustness related issues in speaker recognition

“…But overall, the research work on the SUSR task was still in its infancy. Below is an overview of some research directions on SUSR [102].…”