Non-linear predictors based on the functionally expanded neural networks for speech feature extraction

Chetouani, M.; Hussain, A.; Gas, B.; Zarader, J.-L.

Abstract

In this paper we focus on the design of the feature extractor stage of the speech recognition system which aims to compute optimal vectors for the next phoneme classification stage. We propose a new non-linear feature extraction method based on the linear-in-parameters functionally expanded neural network (FENN) model. The main idea is to design an improved and flexible feature extractor which can effectively account for some of the significant non-linear phenomena usually observed in the speech production process. The effectiveness of the proposed method is assessed on phoneme classification tasks. Specifically, we evaluate the performances on the telephone quality NTIMIT database, focusing the investigations on highly confusable phonemes such as front vowels: /ih/, /ey/, /eh/, /ae/. The results are compared with other widely used coding methods namely, the linear predictive coding (LPC) and the Mel frequency cepstral coding (MFCC). The experiments show a relative improvement in the rates through the use of our proposed non-linear feature extractor technique.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 2006 IEEE International Conference on Engineering of Intelligent Systems
• Start Date: Apr 22, 2006
• End Date: Apr 23, 2006
• Online Publication Date: Sep 18, 2006
• Publication Date: 2006
• Deposit Date: Oct 16, 2019
• Book Title: 2006 IEEE International Conference on Engineering of Intelligent Systems
• ISBN: 1-4244-0456-8
• DOI: https://doi.org/10.1109/ICEIS.2006.1703129
• Keywords: feature extraction, neural nets, prediction theory, signal classification, speech processing, vectors
• Public URL: http://researchrepository.napier.ac.uk/Output/1793643