Title:	FEATURE FUSION BASED ON AUDITORY AND SPEECH SYSTEMS FOR AN IMPROVED VOICE BIOMETRICS SYSTEM USING ARTIFICIAL NEURAL NETWORK
Author(s):	Youssouf Ismail Cherifi and Abdelhakim Dahimene
ISBN:	978-989-8704-21-4
Editors:	Yingcai Xiao, Ajith P. Abraham and Jörg Roth
Year:	2020
Edition:	Single
Keywords:	Speech Processing, Neural Network, Pattern Recognition, Speaker Recognition, Feature Extraction
Type:	Short
First Page:	188
Last Page:	196
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	In today’s world, identifying a speaker has become an essential task. Especially for systems that rely on voice commands or speech in general to operate. These systems use speaker-specific features to identify the individual, features such as Mel Frequency Cepstral Coefficients, Linear Predictive Coding, or Perceptual Linear Predictive. Although these features provide different representations of speech, they can all be considered as either auditory system based (type 1) or speech system based (type 2). In this work, a method for improving existing voice biometrics system is presented. A method fusing a type 1 feature with a type 2 feature is implemented using artificial neural network and evaluated on in-campus recorded data set. The obtained results demonstrate the potential of our approach in improving voice biometrics system, regardless of the underlying task being speaker identification or verification.
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