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Title: A class-dependent background model for speech signal feature extraction
Authors: Jiang, Y 
Leung, HF 
Issue Date: Nov-2018
Source: 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), Shanghai, China, China, 19-21 Nov. 2018, p. 1-5
Abstract: Universal Background Model (UBM) has been successfully applied to many speech signal classification tasks, such as speaker recognition and microphone recognition. UBM is used to form Gaussian Supervector (GSV) or i-vector, which is a good feature vector representing a piece of speech signal. In this paper, we propose another background model called Class-dependent Background Model (CBM), which makes use of class labels. UBM is completely a generative model, while CBM can be both generative and discriminative. Under some conditions, CBM can consume less time to be constructed than UBM. We also compare the performance of UBM and CBM as the background model to form GSV and i-vector for doing speaker recognition, microphone recognition, and telephone session recognition. Experimental results show that CBM performs very well and can be even better than UBM in most cases.
Keywords: Class-dependent background model
Universal background model
Gaussian supervector
I-vector
Speech signal classification
Publisher: Institute of Electrical and Electronics Engineers
ISBN: 978-1-5386-6811-5 (Electronic)
978-1-5386-6810-8 (USB)
978-1-5386-6812-2 (Print on Demand(PoD))
DOI: 10.1109/ICDSP.2018.8631583
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication Y. Jiang and H. F. Frank Leung, "A Class-dependent Background Model for Speech Signal Feature Extraction," 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), Shanghai, China, 2018, pp. 1-5 is available at https://dx.doi.org/10.1109/ICDSP.2018.8631583
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