文章基本信息

标题：High level speaker specific features modeling in automatic speaker recognition system
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作者：Satyanand Singh
期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2020
卷号：10
期号：2
页码：1859-1867
DOI：10.11591/ijece.v10i2.pp1859-1867
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：Spoken words convey several levels of information. At the primary level, the speech conveys words or spoken messages, but at the secondary level, the speech also reveals information about the speakers. This work is based on the high-level speaker-specific features on statistical speaker modeling techniques that express the characteristic sound of the human voice. Using Hidden Markov model (HMM), Gaussian mixture model (GMM), and Linear Discriminant Analysis (LDA) models build Automatic Speaker Recognition (ASR) system that are computational inexpensive can recognize speakers regardless of what is said. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using a standard TIMIT speech corpus. The ASR efficiency of HMM, GMM, and LDA based modeling technique are 98.8%, 99.1%, and 98.6% and Equal Error Rate (EER) is 4.5%, 4.4% and 4.55% respectively. The EER improvement of GMM modeling technique based ASR systemcompared with HMM and LDA is 4.25% and 8.51% respectively.
关键词：automatic speaker recognition (ASR);extreme learning machine (ELM);gaussian mixer model (GMM);hidden markov model (HMM);linear discriminant analysis (LDA);support vector machines (SVM);universal background model (UBM);