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Speaker recognition with hybrid features from a deep belief network
journal contribution
posted on 2023-05-20, 17:25 authored by Ali, H, Son TranSon Tran, Benetos, E, d'Avila Garcez, ASLearning representation from audio data has shown advantages over the handcrafted features such as mel-frequency cepstral coefficients (MFCCs) in many audio applications. In most of the representation learning approaches, the connectionist systems have been used to learn and extract latent features from the fixed length data. In this paper, we propose an approach to combine the learned features and the MFCC features for speaker recognition task, which can be applied to audio scripts of different lengths. In particular, we study the use of features from different levels of deep belief network for quantizing the audio data into vectors of audio word counts. These vectors represent the audio scripts of different lengths that make them easier to train a classifier. We show in the experiment that the audio word count vectors generated from mixture of DBN features at different layers give better performance than the MFCC features. We also can achieve further improvement by combining the audio word count vector and the MFCC features.
History
Publication title
Neural Computing and ApplicationsVolume
29Issue
6Pagination
13-19ISSN
0941-0643Department/School
School of Information and Communication TechnologyPublisher
Springer-VerlagPlace of publication
175 Fifth Ave, New York, USA, Ny, 10010Rights statement
Copyright 2016 The Natural Computing Applications ForumRepository Status
- Restricted