Automatic voice recognition using traditional and artificial neural network approaches

The main objective of this research is to develop an algorithm for isolated-word recognition. This research is focused on digital signal analysis rather than linguistic analysis of speech. Features extraction is carried out by applying a Linear Predictive Coding (LPC) algorithm with order of 10. Continuous-word and speaker independent recognition will be considered in future study after accomplishing this isolated word research. To examine the similarity between the reference and the training sets, two approaches are explored. The first is implementing traditional pattern recognition techniques where a dynamic time warping algorithm is applied to align the two sets and calculate the probability of matching by measuring the Euclidean distance between the two sets. The second is implementing a backpropagation artificial neural net model with three layers as the pattern classifier. The adaptation rule implemented in this network is the generalized least mean square (LMS) rule. The first approach has been accomplished. A vocabulary of 50 words was selected and tested. The accuracy of the algorithm was found to be around 85 percent. The second approach is in progress at the present time

Digital signal processing algorithms for automatic voice recognition

The current digital signal analysis algorithms are investigated that are implemented in automatic voice recognition algorithms. Automatic voice recognition means, the capability of a computer to recognize and interact with verbal commands. The digital signal is focused on, rather than the linguistic, analysis of speech signal. Several digital signal processing algorithms are available for voice recognition. Some of these algorithms are: Linear Predictive Coding (LPC), Short-time Fourier Analysis, and Cepstrum Analysis. Among these algorithms, the LPC is the most widely used. This algorithm has short execution time and do not require large memory storage. However, it has several limitations due to the assumptions used to develop it. The other 2 algorithms are frequency domain algorithms with not many assumptions, but they are not widely implemented or investigated. However, with the recent advances in the digital technology, namely signal processors, these 2 frequency domain algorithms may be investigated in order to implement them in voice recognition. This research is concerned with real time, microprocessor based recognition algorithms

A Framework for Designing MIMO systems with Decision Feedback Equalization or Tomlinson-Harashima Precoding

We consider joint transceiver design for general Multiple-Input Multiple-Output communication systems that implement interference (pre-)subtraction, such as those based on Decision Feedback Equalization (DFE) or Tomlinson-Harashima precoding (THP). We develop a unified framework for joint transceiver design by considering design criteria that are expressed as functions of the Mean Square Error (MSE) of the individual data streams. By deriving two inequalities that involve the logarithms of the individual MSEs, we obtain optimal designs for two classes of communication objectives, namely those that are Schur-convex and Schur-concave functions of these logarithms. For Schur-convex objectives, the optimal design results in data streams with equal MSEs. This design simultaneously minimizes the total MSE and maximizes the mutual information for the DFE-based model. For Schur-concave objectives, the optimal DFE design results in linear equalization and the optimal THP design results in linear precoding. The proposed framework embraces a wide range of design objectives and can be regarded as a counterpart of the existing framework of linear transceiver design.Comment: To appear in ICASSP 200

GEODESICS IN LORENTZIAN MANIFOLDS

We present an extension of Geodesics in Lorentzian Manifolds (Semi-Riemannian Manifolds or pseudo-Riemannian Manifolds ). A geodesic on a Riemannian manifold is, locally, a length minimizing curve. On the other hand, geodesics in Lorentzian manifolds can be viewed as a distance between ``events\u27\u27. They are no longer distance minimizing (instead, some are distance maximizing) and our goal is to illustrate over what time parameter geodesics in Lorentzian manifolds are defined. If all geodesics in timelike or spacelike or lightlike are defined for infinite time, then the manifold is called ``geodesically complete\u27\u27, or simply, ``complete\u27\u27. It is easy to show that the magnitude of a geodesic is constant, so one can characterize geodesics in terms of their causal character: if this magnitude is negative, the geodesic is called timelike. If this magnitude is positive, then it is spacelike. If this magnitude is 0, then it is called lightlike or null. Geodesic completeness can be considered by only considering one causal character to produce the notions of spacelike complete, timelike complete, and null or lightlike complete. We illustrate that some of the notions are inequivalent

Determining The Effects of Fulvic acid on Biofilm/Planktonic Streptococcus Mutans Growth

poster abstractFulvic acid, a major organic compound extract of Shilajit has been the focus of dental research for the past few years. Shilajit, a sticky tar-like substance of dark brownish color, was used during the ancient times, thousands of years ago and continues to be the traditional method today in India to aid with curing bone/cartilage diseases. Shilajit has also been proven to have anti-inflammatory and pain suppressing effects. This experiment determined the minimum inhibitory concentration (MIC), which is the lowest concentration of fulvic acid, an active component of shilajit that inhibits the visible growth of S. mutans. This experiment also determined the minimum bactericidal concentration (MBC) which is the lowest concentration of fulvic acid that kills S. mutans. A 3-day procedure to determine the growth vs inhibition of the S. mutans was conducted and bacterial readings were recorded using a spectrophotometer after treating S. mutans with 10% formaldehyde, crystal violet stain, and iso-propanol with 30-45 minute incubations between each. The experiment determined that very high concentrations of fulvic acid killed S. mutans, while less concentrated fulvic acid inhibited the growth of S. mutans bacterial cells. A solution comprised of a 5% concentration of fulvic acid killed all of the S. mutans; 5.00%, 2.50%, and 1.25% fulvic acid concentrations had bacterial absorbance of 0.000, 0.009, and 0.027, respectively, as compared to the control group’s normal bacterial growth absorbance of 0.254. Additionally, solutions ranging from a two-fold dilution of fulvic acid to six-fold dilution of fulvic acid inhibited the growth of S. mutans. A similar trend was also observed in planktonic and biofilm formation. For all of the above, in the seventh and eighth dilution (0.078% and 0.039% respectively) of the fulvic acid, the growth of S. mutans bacteria was similar to the control group due to the level of dilution. Overall it was observed that fulvic acid is able to kill bacteria in strong concentrations. Additionally it is able to inhibit further growth of bacteria in lower concentrations, but once the solution becomes too dilute, it does not have an effect on bacterial growth. This contributes greatly to the field of oral health because this data can be utilized for further research on oral bacterial growth inhibitors. Furthermore, the data collected here is a significant starting point for research on the specific minimum concentrations necessary to inhibit oral bacteria growth, because this can be used to determine the smallest amounts of fulvic acid, the bacteria the human body can handle