Time-Frequency Cepstral Features and Heteroscedastic Linear Discriminant Analysis for Language Recognition

Abstract—The shifted delta cepstrum (SDC) is a widely used feature extraction for language recognition (LRE). With a high context width due to incorporation of multiple frames, SDC outperforms traditional delta and acceleration feature vectors. However, it also introduces correlation into the concatenated feature vector, which increases redundancy and may degrade the performance of backend classifiers. In this paper, we first propose a time–frequency cepstral (TFC) feature vector, which is obtained by performing a temporal discrete cosine transform (DCT) on the cepstrum matrix and selecting the transformed elements in a zigzag scan order. Beyond this, we increase discriminability through a heteroscedastic linear discriminant analysis (HLDA) on the full cepstrum matrix. By utilizing block diagonal matrix constraints, the large HLDA problem is then reduced to several smaller HLDA problems, creating a block diagonal HLDA (BDHLDA) algorithm which has much lower computational complexi...

• Type: Journal
• Year: 2011
• Where: TASLP
• Authors: Weiqiang Zhang, Liang He, Yan Deng, Jia Liu, M. T. Johnson
• Keywords: Cepstrum Matrix, Feature Vector, Hlda Problems, Software Engineering, TASLP 2011