A Speech Recognition System Based on Hybrid Wavelet Network Including a Fuzzy Decision Support System Olfa Jemai, Ridha Ejbali, Mourad Zaied, and Chokri Ben Amar REGIM-Lab.: REsearch Groups in Intelligent Machines, University of Sfax, ENIS, BP 1173, Sfax, 3038, Tunisia olfa.jemai@ieee.org, ridha_ejbali@ieee.org, mourad.zaied@ieee.org, chokri.benamar@ieee.org ABSTRACT This paper aims at developing a novel approach for speech recognition based on wavelet network learnt by fast wavelet transform (FWN) including a fuzzy decision support system (FDSS). Our contributions reside in, first, proposing a novel learning algorithm for speech recognition based on the fast wavelet transform (FWT) which has many advantages compared to other algorithms and in which major problems of the previous works to compute connection weights were solved. They were determined by a direct solution which requires computing matrix inversion, which may be intensive. However, the new algorithm was realized by the iterative application of FWT to compute connection weights. Second, proposing a new classification way for this speech recognition system. It operated a human reasoning mode employing a FDSS to compute similarity degrees between test and training signals. Extensive empirical experiments were conducted to compare the proposed approach with other approaches. Obtained results show that the new speech recognition system has a better performance than previously established ones. Keywords: Speech recognition, Fast training algorithm, Fuzzy decision support system. 1. INTRODUCTION Speech is one of the most natural ways human beings exchange information. In recent years, extensive research has been done to develop machines that can understand and produce speech as humans do so naturally. Therefore, computer scientists have been researching ways and means to enable people to talk to a computer, by making it recognize what they say. The list of applications of automatic speech recognition is increasingly long. Literature has indicated wavelets and wavelet networks as a promising approach for many applications. They were both used in [1, 2] in order to recognize driver eyes states to inhibit the hypo-vigilance. In [3] and [4], hand gestures recognition was established using the FWN in order to command computers. In [5], wavelets network was used to approximate the acoustic units for the task of the speech recognition. The authors in [6, 7] proposed a new method of 2D and 3D facial recognition based on a compact and representative biometric signature produced by means of wavelet networks. Also, in [8], it was used for images copies detection and in [9] for content based image retrieval and for image compression in [10]. Therefore, wavelet networks have widely proved their effectiveness in the classification domain [11, 12]. Furthermore, a recent paper in this direction [12, 13] has developed an efficient algorithm which effectively classifies different datasets. The main challenge for us is to propose a novel learning algorithm based on the fast wavelet transform (FWT) for speech recognition and include a fuzzy decision support system (FDSS) to compute similarity degrees between the query and the training signals in order to ensure the decision-making phase. The remainder of the paper is organized as follows: Section 2 gives a review of the wavelet network architecture and its learning algorithm. Section 3 outlines the proposed approach for speech recognition. Section 4 presents the experimental results with the aim of illustrating the effectiveness of the proposed method. In section 5, we give the conclusion and open perspectives for future works. 2. TECHNICAL BACKGROUND This section presents a FWN classifier model that is able to approximate every sample (D) to produce a data signature. Seventh International Conference on Machine Vision (ICMV 2014), edited by Antanas Verikas, Branislav Vuksanovic, Petia Radeva, Jianhong Zhou, Proc. of SPIE Vol. 9445, 944503 © 2015 SPIE · CCC code: 0277-786X/15/$18 · doi: 10.1117/12.2180554 Proc. of SPIE Vol. 9445 944503-1 Trial Version www.nuance.com