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:	Refereed Article
Keywords:	deep belief networks, deep learning, mel-frequency cepstral coefficients, speaker recognition
Research Division:	Information and Computing Sciences
Research Group:	Artificial intelligence
Research Field:	Intelligent robotics
Objective Division:	Culture and Society
Objective Group:	Communication
Objective Field:	The media
UTAS Author:	Tran, SN (Dr Son Tran)
ID Code:	140699
Year Published:	2018
Web of Science® Times Cited:	7
Deposited By:	Information and Communication Technology
Deposited On:	2020-09-01
Last Modified:	2020-10-23
Downloads:	0