The speech spectrum is very sensitive to linear predictive coding (LPC) parameters, so small quantization errors may cause unstable synthesis filter. Line spectral pairs (LSPs) are more efficient representations than LPC parameters. On the other hand, artificial neural networks (ANNs) have been used successfully to improving the quality and also reduction the computational complexity of […]
Variations of speech parameters due to emotion or stress are noticeable. In the presence of such variations, if a neutral model is used for the system, the speech recognition accuracy deteriorates. The evaluation of how emotion influences speech parameters is the first step towards emotional speech recognition. Pitch frequency is an important parameter in speech […]
Emotion has an important role in naturalness of man-machine communication. So, computerized emotion recognition from speech is investigated by many researchers in the recent decades. In this paper, the effect of formant-related features on improving the performance of emotion detection systems is experimented. To do this, various forms and combinations of the first three formants […]
To make human–computer interaction (HCI) more natural and friendly, it would be beneficial to give computers the ability to recognize situations the same way a human does. Naturally, people use a spontaneous combination of face, body gesture and speech to express their feelings. In this paper we simulate human perception of emotion with emotion related […]
In this paper, we use a nonlinear hierarchical model predictive control (MPC) to stabilize the Segway robot. We also use hardware in the loop (HIL) simulation in order to model the delay response of the wheels' motor and verify the control algorithm. In Two-Wheeled Personal Transportation Robots (TWPTR), changing the center of mass location and […]
