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In this paper, the performance of mUlti-taper spectral estimate is investigated relative to conventional single taper estimate for the application of emotion recognition from speech signals. Typically, a single taper/window helps in reducing bias of the estimate, but due to its high variance, the resulting spectral features tend to give poor recognition performance. The weighted averages of the multi-tapered uncorrelated eigen spectra results in more discriminative spectral features, thus increasing the overall performance. We demonstrate that the application of six Multi-peak mUlti-tapers with support vector machine results in 81 % classification accuracy on seven emotions from Berlin emotion database considering only spectral features, compared to 72% using conventional Hamming window method.

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Wireless communication is a promising technology for a wide range of applications from TV remote control to satellite based TV systems. As the need for high data rate is increasing day by day the need for multicarrier communication has come in to picture. Orthogonal frequency division multiplexing (OFDM) is a multicarrier transmission technique used for high data rate wireless transmission. In OFDM the transmitter modulates the message bit sequence in to symbols, performs IFFT, converts in to time domain signal and transmits through a wireless channel. The information usually get distorted due to characteristics of the channel So it is required to estimate the channel characteristics and compensate at the receiver to recover the information sent. In this paper we explore two estimation techniques Least Square (LS) and Minimum Mean Square Error (MMSE) using International Telecommunication Union (ITU) vehicular A channel model, further symbol error rate is calculated for different Doppler frequencies.