说话人识别中的分数域语速归一化

Score domain speaking rate normalization for speaker recognition

语速变化导致说话人识别系统性能显著下降。该文提出一种分数域语速归一化方法来降低语速变化对说话人识别系统的影响。由不同语速语音数据组成参考集（全局和局部）,对每一个登入说话人估计其对参考集中每一类参考语音的分数分布,局部参考集通过按相对语速划分全局参考集而获得。基于该文录制的语速数据库在GMM-UBM（Gaussian mixture model-universal background model）框架下对测试语音进行分数归一化,并通过训练数据扩展有效解决了数据系数问题,最终等错误率相对下降33.33%。研究结果表明：全局和局部归一化方法都有效减少了语速变化对说话人识别系统的影响。

Speaking rate variations seriously degrade speaker recognition accuracy.This paper presents a normalization approach in the score domain that reduces the impact of speaking rate variations.The score distributions for each type of imposter in the cohort set（global and local sets which consist of speech utterances at different speaking rates）are computed against each enrolled speaker with the local cohort set obtained by splitting the utterances in the global cohort set according to the relative speaking rates.The scores for the test speech are normalized based on a self-recorded speaking rate database using a GMM-UBM（Gaussian mixture model-universal background model） framework with the data sparsity problem handled by augmenting the training data with a final relative EER（equal error rate）reduction of 33.33%.This study shows that global and local score normalization methods effectively reduce the impact of speaking rate variations on speaker recognition.