5th International Conference on Spoken Language Processing

Sydney, Australia
November 30 - December 4, 1998

Modular Connectionist Systems for Identifying Complex Arabic Phonetic Features

Sid-Ahmed Selouani (1), Jean Caelen (2)

(1) IE/Houari boumedienne University of science and technology, Algeria
(2) IMAG/UJF, France

This paper concerns the identification of Arabic macro-classes and phonetic features by systems using a hierarchy of neural networks. These systems are composed of sub-neural-networks (SNNs) carrying out binary discrimination sub-tasks. Two types of architecture are presented: serial structure of experts and parallel disposition of them. This mixture of experts is composed of typically time delay neural networks using a version of autoregressive backpropagation algorithm (AR-TDNN). These hierarchical configurations are confronted to a monolithic system using standard backpropagation learning procedure. The test database consists of 60 VCV utterances and 50 phrases pronounced by 6 Algerian native speakers. The parallel configuration achieved much fewer error rate (13% vs. 16% and 28%) than other architectures. The parallel mixture of experts is incorporated in a hybrid structure (HMM-SNN) in the order to enhance performances of standard HMMs. Identification results show that 10% reduction of error rate is obtained by the hybrid system.

Full Paper

Bibliographic reference.  Selouani, Sid-Ahmed / Caelen, Jean (1998): "Modular connectionist systems for identifying complex arabic phonetic features", In ICSLP-1998, paper 0358.