基于支持说话人权重的快速说话人自适应算法

A Rapid Speaker Adaptation Based on Support Speaker Weighting

针对语音识别系统中快速说话人自适应问题,提出了一种支持说话人权重算法.该算法通过支持说话人的计算实现了说话人选择与自适应参数的降维,减少了自适应时的存储量,有效提高了自适应数据较少时的性能.有监督自适应的实验结果表明,在仅有一句自适应语句的情况下系统误识率相对非特定人(SI)系统下降了5.82%,明显优于其他快速自适应算法.

A novel model-based speaker adaptation algorithm, support speaker weighting （SSW）, was proposed for rapid speaker adaptation in speech recognition systems. It realizes the specific speaker selection and dimensionality reduction by computing the support speaker subsets from many reference speakers. This method yields major improvements in performance for tiny amounts of adaptation data while greatly reducing the memory requirement. The experiments on the supervised adaptation demonstrate that the relative error rate reduction of 5.82% is achieved when only one adaptation sentence is available. In comparison with other rapid speaker adaptation algorithms, SSW is more effective.