NOTE: The following materials are presented for timely
dissemination of academic and technical work. Copyright and all other rights
therein are reserved by authors and/or other copyright holders. Persoanl
use of the following materials is permitted and, however, people using
the materials or information are expected to adhere to the terms and
constraints invoked by the related copyright.
Capture Inter-Speaker Information with a Neural Network
for Speaker Identification
ABSTRACT
Model-based approach is one of methods widely used for speaker
identification, where a statistical model is used to characterize
a specific speaker's voice but no inter-speaker information is
involved in its parameter estimation. It is observed that inter-speaker
information is very helpful in discriminating between different speakers.
In this paper, we propose a novel method for the use of inter-speaker
information to improve performance of a model-based speaker identification
system. A neural network is employed to capture the inter-speaker
information from the output space of those statistical models. In order
to sufficiently utilize inter-speaker information, a rival penalized
encoding rule is proposed to design supervised learning pairs. For better
generalization, moreover, a query-based learning algorithm is presented
to actively select the input data of interest during training of the neural
network. Comparative results on the KING speech corpus show that our method
leads to a considerable improvement for a model-based speaker identification
system.
Click
tnn2002.pdf
for full text