In this work a review of speech technologies and their applications that provide or augment access to the printed or electronic information, the daily or social activities, as well as the private or public facilities for blind or low vision persons i

Speech production has always been a subject of interest both at the morphological and acoustic levels. This knowledge is useful for a better understanding of all the involved mechanisms and for the construction of articulatory models. Magnetic resonance imaging (MRI) is a powerful technique that allows the study of the whole vocal tract, with good soft tissue contrast and resolution, and permits the calculation of area functions towards a better understanding of this mechanism. Thus, our aim is to demonstrate the value and application of MRI in speech production study and its relationship with engineering, namely with biomedical engineering. After vocal tract contours extraction, data were processed for 3D reconstruction culminating in model construction of some of the sounds of European Portuguese. MRI provides useful morphological data about the position and shape of the different speech articulators, and the biomedical engineering computational tools for its analysis.

The harmonic parameters Autocorrelation, Harmonic to Noise Ratio (HNR), and Noise to Harmonic Ratio are related to vocal quality, providing alternative measures of the harmonic energy of a speech signal. They will be used as input resources for an intelligent medical decision support system for the diagnosis of speech pathology. An efficient algorithm is important when implementing it on low-power devices. This article presents an algorithm that determines these parameters by optimizing the window type and length. The method used comparatively analyzes the values of the algorithm, with different combinations of window and size and a reference value. Hamming, Hanning, and Blackman windows with lengths of 3, 6, 12, and 24 glottal cycles and various sampling frequencies were investigated. As a result, we present an efficient algorithm that determines the parameters using the Hanning window with a length of six glottal cycles. The mean difference of Autocorrelation is less than 0.004, and that of HNR is less than 0.42 dB. In conclusion, this algorithm allows extraction of the parameters close to the reference values. In Autocorrelation, there are no significant effects of sampling frequency. However, it should be used cautiously for HNR with lower sampling rates.

This paper presents a new approach for the simultaneous identification of rotor flux components in the rotor reference frame and electrical parameters of a vector controlled induction motor, for real-time implementations, using an extended Kalman filter (EKF) and a reduced order model structure for lower computational effort. The proposed new method requires the measurement of motor speed, stator voltages and currents signals. Using a motor model structure with four electrical parameters, the estimation of flux space phasor and rotor parameters is presented The estimation is subsequently further extended to include the motor stator parameters and the results are analyzed as well as robustness. Simulated and experimental studies highlight the improvements brought by this new approach, mainly, a simple and reduced state equation, the introduced scalar output equation and lower computational cadency by using lower sampling frequencies in the proposed rotor reference frame.