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Proceedings Paper

Feature extraction and representation via orthogonal signal decomposition for parametric speech signal processing
Author(s): Jeffrey Y. Beyon
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Paper Abstract

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.

Paper Details

Date Published: 22 April 2020
PDF: 6 pages
Proc. SPIE 11423, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX, 114230T (22 April 2020); doi: 10.1117/12.2544715
Show Author Affiliations
Jeffrey Y. Beyon, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 11423:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX
Ivan Kadar; Erik P. Blasch; Lynne L. Grewe, Editor(s)

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