Speech recognition by machine may be defined as the conversion of human speech signal into textual form automatically by the machine without any human intervention. Two feature extraction techniques utilizing DWT (Discrete Wavelet Transform) and WPD (Wavelet Packet Decomposition) for speech recognition are discussed in the present article. The comparison of two speech recognizer, first, based on Discrete Wavelet Transform and the second based on Wavelet Packet Decomposition, and with four classifiers has been done in this paper. The proposed method is implemented for a database consisting of ten digits and two hundred speakers, making it a database of 2000 speech samples. The results present the accuracy rate of the respective speech recognizers.
The traditional method of doing business has been disrupted by social media. In order to develop the enterprise, it is essential to forecast the level of interaction that a new post would receive from social media users. It is possible for the user's interest in any one social media post to be impacted by external factors or to dwindle as a result of changes in his behaviour. The popularity detection strategies that are user-based or population-based are unable to keep up with these shifts, which leads to inaccurate forecasts. This work makes a prediction about how popular the post will be and addresses any anomalies caused by factors outside of the study. A novel improved PARAFAC (A-PARAFAC) method that is tensor factorization-based has been presented in order to cope with the user criteria that will be used in the future to rate any project. We consolidated the information on the historically popular content, and we accelerated the computation by choosing the top contents that were most like each other. The tensor is factorised with the application of the Adam optimization. It has been modified such that the bias is now included in the gradient function of A-PARAFAC, and the value of the bias is updated after each iteration. The prediction accuracy is improved by 32.25% with this strategy compared to other state of the art methods.
The tunnel field-effect transistor (TFET) exploits the band to band tunneling (BTBT) phenomenon for carrier injection and this helps to lower the subthreshold swing <60mV/decade due to the absence of thermal (kT/q) dependence. Tunnel Field Effect Transistor has been evolved by T. Baba in 1992, and provides an alternative to conventional switch based on various performance parameters. TFETs show tremendous potential from the low-power applications point of view. However, the challenge to gain a high Ion/Ioff ratio in combination with subthreshold swing <60mV/decade still persists for devices to be used in high-performance circuits. To achieve this, it is required that many technologies to be used in combination for the realization of such hetero-structure TFET devices.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.