Speech processing
Yaser Shekofteh; Farshad Almasganj
Volume 6, Issue 1 , June 2012, , Pages 17-33
Abstract
Recent researches show that nonlinear and chaotic behavior of the speech signal can be studied in the reconstructed phase space (RPS). Delay embedding theorem is a useful tool to study embedded speech trajectories in the RPS. Characteristics of the speech trajectories have rarely used in the practical ...
Read More
Recent researches show that nonlinear and chaotic behavior of the speech signal can be studied in the reconstructed phase space (RPS). Delay embedding theorem is a useful tool to study embedded speech trajectories in the RPS. Characteristics of the speech trajectories have rarely used in the practical speech recognition systems. Therefore, in this paper, a new feature extraction (FE) method is proposed based on parameters of vector AR (VAR) analysis over the speech trajectories. In this method, using filter and reflection matrices obtained from applying VAR analysis on static and dynamic information of the speech trajectory in the RPS, a high-dimensional feature vector can be achieved. Then, different transformation methods are utilized to attain final feature vectors with appropriate dimension. Results of discrete and continuous phoneme recognition over FARSDAT speech corpus show that the efficiency of the proposed FE method is better than other time-domain-based FE methods such as LPC and LPREF.
Speech processing
Ayoub Daliri; Farzad Towhidkhah; Shahriar Gharibzadeh; Yaser Shekofteh
Volume 2, Issue 2 , June 2008, , Pages 123-129
Abstract
Speech production is one of the most complicated physiological systems including different subsystems. These subsystems must work together in a synchronous manner. One of the important sub-systems is the jaw. Although different models have suggested for jaw, no suitable model has been proposed yet to ...
Read More
Speech production is one of the most complicated physiological systems including different subsystems. These subsystems must work together in a synchronous manner. One of the important sub-systems is the jaw. Although different models have suggested for jaw, no suitable model has been proposed yet to consider the interactions between muscles, bones and nervous system. In this paper, using Spring-Damper-Mass and a nonlinear concept, we introduced a novel model for jaw movement during speech production. Experimental data were used to estimate the model parameters. Computer simulation results showed that the model could generate the jaw movement patterns similar to those observed in physiological behavior. Generality and simplicity of the model are two model features useful for more investigation of the jaw movement in different tasks.
