Study of Senone-Based Deep Neural Network Approaches for Spoken Language Recognition

Abstract—This paper compares different approaches for using deep neural networks (DNNs) trained to predict senone posteriors for the task of spoken language recognition (SLR). These approaches have recently been found to outperform various baseline systems on different datasets, but they have not yet been compared to each other or to a common baseline. Two of these approaches use the DNNs to generate feature vectors which are then processed in different ways to predict the score of each language given a test sample. The features are extracted either from a bottleneck layer in the DNN or from the output layer. In the third approach, the standard i-vector extraction procedure is modified to use the senones as classes and the DNN to predict the zero-th order statistics. We compare these three approaches and conclude that the approach based on bottleneck features followed by i-vector modeling outperform the other two approaches. We also show that score-level fusion of some of these appr...

• Type: Journal
• Year: 2016
• Where: TASLP
• Authors: Luciana Ferrer, Yun Lei, Mitchell McLaren, Nicolas Scheffer
• Keywords: Software Engineering, TASLP 2016