短语音说话人确认的质量度量
Quality Measures for Speaker Verification with Short Utterances
摘要：
自动说话人确认（ASV）系统的性能下降，由于用于注册和验证的语音量的减少。结合基于不同特征和分类器的多个系统大大降低了短话语的说话人确认出错率。这项工作试图在系统组合过程中纳入补充信息。我们使用估计的模型参数的质量作为补充信息。我们介绍了一类新的质量措施制定使用零阶充分统计在i向量提取过程中使用。我们已经使用了建议的质量措施作为边信息结合ASV系统的基础上高斯混合模型通用背景模型（GMM-UBM）和i-向量。所提出的方法在NIST SRE语料库上的说话人识别性能有了相当大的提高，特别是在短时间条件下。我们还观察到基于不同持续时间的质量测量的现有系统的改进。
英文摘要：
The performances of the automatic speaker verification (ASV) systems degrade due to the reduction in the amount of speech used for enrollment and verification. Combining multiple systems based on different features and classifiers considerably reduces speaker verification error rate with short utterances. This work attempts to incorporate supplementary information during the system combination process. We use quality of the estimated model parameters as supplementary information. We introduce a class of novel quality measures formulated using the zero-order sufficient statistics used during the i-vector extraction process. We have used the proposed quality measures as side information for combining ASV systems based on Gaussian mixture model-universal background model (GMM-UBM) and i-vector. The proposed methods demonstrate considerable improvement in speaker recognition performance on NIST SRE corpora, especially in short duration conditions. We have also observed improvement over existing systems based on different duration-based quality measures.
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