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Learning Deep Embedding with Acoustic and Phoneme Features for Speaker Recognition in FM Broadcasting.

Authors :
Li, Xiao
Chen, Xiao
Fu, Rui
Hu, Xiao
Chen, Mintong
Niu, Kun
Source :
IET Biometrics (Wiley-Blackwell); 3/22/2024, Vol. 2024, p1-10, 10p
Publication Year :
2024

Abstract

Text-independent speaker verification (TI-SV) is a crucial task in speaker recognition, as it involves verifying an individual's claimed identity from speech of arbitrary content without any human intervention. The target for TI-SV is to design a discriminative network to learn deep speaker embedding for speaker idiosyncrasy. In this paper, we propose a deep speaker embedding learning approach of a hybrid deep neural network (DNN) for TI-SV in FM broadcasting. Not only acoustic features are utilized, but also phoneme features are introduced as prior knowledge to collectively learn deep speaker embedding. The hybrid DNN consists of a convolutional neural network architecture for generating acoustic features and a multilayer perceptron architecture for extracting phoneme features sequentially, which represent significant pronunciation attributes. The extracted acoustic and phoneme features are concatenated to form deep embedding descriptors for speaker identity. The hybrid DNN demonstrates not only the complementarity between acoustic and phoneme features but also the temporality of phoneme features in a sequence. Our experiments show that the hybrid DNN outperforms existing methods and delivers a remarkable performance in FM broadcasting TI-SV. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20474938
Volume :
2024
Database :
Complementary Index
Journal :
IET Biometrics (Wiley-Blackwell)
Publication Type :
Academic Journal
Accession number :
177439424
Full Text :
https://doi.org/10.1049/2024/6694481