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Abstract

Learning 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.

Item Type:	Article
Authors/Creators:	Ali, H and Tran, SN and Benetos, E and d'Avila Garcez, AS
Keywords:	deep belief networks, deep learning, mel-frequency cepstral coefficients, speaker recognition
Journal or Publication Title:	Neural Computing and Applications
Publisher:	Springer-Verlag
ISSN:	0941-0643
DOI / ID Number:	10.1007/s00521-016-2501-7
Copyright Information:	Copyright 2016 The Natural Computing Applications Forum
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