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.
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A useful survey is presented by Kinnunen et al. [2] on the use of MFCCs and other features such as super vectors for speaker recognition.
Besides the work reviewed in this section, a more recent work has been reported lately in [3], which presents a deep neural network approach for speaker recognition task.
A useful tutorial on SVM is available from Burges [17].
The dataset can be requested via email.
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Acknowledgments
The authors would like to thank Nasir Ahmad, University of Engineering and Technology Peshawar Pakistan and Tillman Weyde, City University London for their useful feedback during this work.
Hazrat Ali is grateful for funding from the Erasmus Mundus Strong Ties Grant. Emmanouil Benetos was supported by the UK AHRC-funded Project `Digital Music Lab-Analysing Big Music Data', Grant No. AH/L01016X/1 and is supported by a UK RAEng Research Fellowship, grant no. RF/128. Hazrat and Son have equal contribution to the paper.
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Ali, H., Tran, S.N., Benetos, E. et al. Speaker recognition with hybrid features from a deep belief network. Neural Comput & Applic 29, 13–19 (2018). https://doi.org/10.1007/s00521-016-2501-7
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DOI: https://doi.org/10.1007/s00521-016-2501-7