KEYWORDS: Matrices, Signal to noise ratio, Feature extraction, Signal processing, Speech recognition, Data processing, Detection and tracking algorithms, Speaker recognition, Pattern recognition, Linear filtering
This research focuses on parametric word recognition applications using the orthogonal signal decomposition method. Despite the popularity of the statistical approach in speech recognition, the parametric approach is very common for simple word applications due to its less computational costs. Higher recognition rates can be obtained by incorporating numerous elements in the feature vector, but this requires greater computational costs, which may not be suitable for applications that demand rapid decision making with a system on a budget. In this investigation, the feature vectors were constructed by means of singular values from the orthogonal signal decomposition method and their effectiveness was tested in realtime word recognition applications. When the theoretical foundation was established and confirmed, it was validated in a program using a sound card.
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