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Automatic Vowel Recognition with GPU based Holographic Neural Network

Kjeldsen, H. D. (2008) Automatic Vowel Recognition with GPU based Holographic Neural Network. Master's Thesis / Essay, Artificial Intelligence.

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Distributed representation in the brain implies that neural representations are patterns of ac-tivity over many neurons and that the same neurons participate in many patterns. Holographic neural networks achieve distributed representation through a mathematical analogy to optical holography. Besides being aesthetically pleasing, the holographic analogy promises highly effective search and inference capabilities and allows robust representations that degrade gracefully. We build upon existing holographic neural networks and implement an important improve-ment to the paradigm that removes a previous restriction to feature vectors that are of ran-dom distribution. This means that it is no longer necessary to map between natural (not ran-dom) signal features and a set of random features; instead the signal features can be used directly. We develop a simple holographic neural network classifier and apply it to the AI-task of automatic vowel recognition with good results that demonstrate the feasibility of the im-provement. The system features a very simple learning scheme adapted from earlier holographic neural networks and uses a parallel graphics processing unit to accelerate both learning and classi-fication. We also give suggestions for further research on holographic neural networks aimed at more difficult AI-tasks, like automatic speech recognition.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:45
Last Modified: 15 Feb 2018 07:45

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