Improved i-Vector Representation for Speaker Diarization
Improved i-Vector Representation for Speaker Diarization
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DOI：
10.1007/s00034-015-0206-2
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发表时间：
2015-12
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影响因子：
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通讯作者：
Yan Xu;I. Mcloughlin;Yan Song;Kui Wu
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文献类型：
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作者：
Yan Xu;I. Mcloughlin;Yan Song;Kui Wu
本文提出使用先前经过良好训练的深度神经网络（DNN）来增强用于说话者日记化的i向量表示。实际上，我们将通常用于训练通用背景模型（UBM）的高斯混合模型替换为使用不同大规模数据集训练的DNN。为了训练T矩阵，我们使用从DNN获得的监督UBM，使用滤波器组输入特征来计算后验信息，然后使用MFCC特征来训练UBM，而不是传统的从单个特征导出的无监督UBM。接下来，我们联合使用DNN和MFCC特征来计算零阶和一阶Baum-Welch统计量，以训练提取器，从中获得i向量。该系统将被证明是实现显着改善NIST 2008年说话人识别评估电话数据任务相比，国家的最先进的方法。
This paper proposes using a previously well-trained deep neural network (DNN) to enhance the i-vector representation used for speaker diarization. In effect, we replace the Gaussian mixture model typically used to train a universal background model (UBM), with a DNN that has been trained using a different large-scale dataset. To train theT-matrix, we use a supervised UBM obtained from the DNN using filterbank input features to calculate the posterior information and then MFCC features to train the UBM instead of a traditional unsupervised UBM derived from single features. Next we jointly use DNN and MFCC features to calculate the zeroth- and first-order Baum–Welch statistics for training an extractor from which we obtain the i-vector. The system will be shown to achieve a significant improvement on the NIST 2008 speaker recognition evaluation telephone data task compared to state-of-the-art approaches.