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Speech Emotion Recognition Using Local and Global Features Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Speaker Verification Method Based on Two-Layer GMM-UBM Model in the Complex Environment

Authors : Qiang He, Zhijiang Wan, Haiyan Zhou, Jie Yang, Ning Zhong

Published in: Brain Informatics

Publisher: Springer International Publishing

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Abstract

In order to improve speaker verification accuracy in the complex environment, a two-layer Gaussian mixture model-universal background model (GMM-UBM) model based on speaker verification method is proposed. For different layer, a GMM-UBM model was trained by different combination of speech features. The voice data of 3 days (36 h) were recorded from the complex environment, and the collected data was manually segmented into four classes: quiet, noise, target speaker and other speaker. Not only the segment data can be used to train GMM-UBM model, but also it can provide a criterion to assess the effectiveness of the model. The results show that the highest recall for the second and third day were 0.75 and 0.74 respectively, and the corresponding specificity were 0.29 and 0.19, which indicates the proposed GMM-UBM model is viable to verify the target speaker in the complex environment.

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previous chapter A Study on Automatic Sleep Stage Classification Based on Clustering Algorithm
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Metadata
Title
Speaker Verification Method Based on Two-Layer GMM-UBM Model in the Complex Environment
Authors
Qiang He
Zhijiang Wan
Haiyan Zhou
Jie Yang
Ning Zhong
Copyright Year
2017
Book
Brain Informatics

Print ISBN: 978-3-319-70771-6

Electronic ISBN: 978-3-319-70772-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-70772-3_14

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