Current manufacturing process controls are principally based only on statistical performance. The next evolution is to make physics based models combined with the state of the art sensors and actuators to control the manufacturing processes. In this paper, metal inert gas welding is used as an example of how the first steps in developing a reliable estimation technique to implement a physics based controller. The weld bead geometry will be the main focus because it is crucial to creating a quality weld. This paper uses an IR camera to generate and evaluate multiple weld bead width estimation techniques and characterizes their corresponding standard deviations. Also a Gaussian Mixture Model (GMM) is used to fit the temperature linescan data to fit an analytical function to the numerical data. The GMM is then used to estimate the weld bead width. Finally, the optimal linescan location is calculated to produce the best possible weld bead estimation. The result is that only one of the estimation techniques actually follows a step input and vi the optimal linescan location is 4 mm from the back of the arc. Furthermore, the GMM provides an excellent fit to the temperature linescan, but does not increase the accuracy of the estimate. / text

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Today's world consists of many ways to communicate information. One of the most effective ways to communicate is through the use of speech. Unfortunately many lose the ability to converse. This in turn leads to a large negative psychological impact. In addition, skills such as lecturing and singing must now be restored via other methods. The usage of text-to-speech synthesis has been a popular resolution of restoring the capability to use oral speech. Text to speech synthesizers convert text into speech. Although text to speech systems are useful, they only allow for few default voice selections that do not represent that of the user. In order to achieve total restoration, voice conversion must be introduced. Voice conversion is a method that adjusts a source voice to sound like a target voice. Voice conversion consists of a training and converting process. The training process is conducted by composing a speech corpus to be spoken by both source and target voice. The speech corpus should encompass a variety of speech sounds. Once training is finished, the conversion function is employed to transform the source voice into the target voice. Effectively, voice conversion allows for a speaker to sound like any other person. Therefore, voice conversion can be applied to alter the voice output of a text to speech system to produce the target voice. The thesis investigates how one approach, specifically the usage of voice conversion using Gaussian mixture modeling, can be applied to alter the voice output of a text to speech synthesis system. Researchers found that acceptable results can be obtained from using these methods. Although voice conversion and text to speech synthesis are effective in restoring voice, a sample of the speaker before voice loss must be used during the training process. Therefore it is vital that voice samples are made to combat voice loss.

This work aims at a task of speaker diarization. The goal is to implement a system which is able to decide "who spoke when". Particular components of implementation are described. The main parts are feature extraction, voice activity detection, speaker segmentation and clustering and finally also postprocessing. This work also contains results of implemented system on test data including a description of evaluation. The test data comes from the NIST RT Evaluation 2005 - 2007 and the lowest error rate for this dataset is 18.52% DER. Results are compared with diarization system implemented by Marijn Huijbregts from The Netherlands, who worked on the same data in 2009 and reached 12.91% DER.