FCSR - Fuzzy Continuous Speech Recognition Approach for Identifying Laryngeal Pathologies Using New Weighted Spectrum Features

Abstract

Speech processing technologies have provided distinct contributions for identifying laryngeal pathology, in which samples of normal and pathologic voice are evaluated. In this paper, a novel Fuzzy Continuous Speech Recognition approach termed FCSR is proposed for laryngeal pathology identification. First of all, new speech weighted spectrum features based on Jacobi–Fourier Moments (JFMs) are presented for characterization of larynx pathologies. This is primarily motivated by the assumption that the energy represented by spectrogram would entirely change with some larynx pathologies like physiological pathologies, neuromuscular pathologies, while it would extremely change with normal speech. This phenomenon would extensively influence the allocation of spectrogram local energy in time axis together with frequency axis. Consequently, the JFMs computed from spectrogram local regions are utilized to characterize distribution of spectrogram local energy. Besides, a proposed multi-class fuzzy support vector machine (FSVM) model is constructed to classify larynx pathologies, where partition index maximization (PIM) clustering along with particle swarm optimization (PSO) are employed for calculating fuzzy memberships and optimizing the arguments of the kernel function of the FSVM, respectively. Eventually, the experiments legitimize the proposed approach in reference to the accuracy of the laryngeal pathology recognition.