Abstract
The aim of this work is to develop a flexible and efficient approach to the classification of the ratio of voiced to unvoiced excitation sources in continuous speech. To achieve this aim we adopt a probabilistic neural network approach. This is accomplished by designing a multi layer perceptron classifier trained by steepest descent minimization of the Least Relative Entropy (LRE) cost function. By using the LRE cost function we can directly output the ratio, as a probability, of excitation source, voiced to unvoiced, for a given speech segment. These output probabilities can then be used directly in other applications, such as low bit rate coders.
Original language | English (US) |
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Pages | 2976-2980 |
Number of pages | 5 |
State | Published - 1999 |
Externally published | Yes |
Event | International Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA Duration: Jul 10 1999 → Jul 16 1999 |
Other
Other | International Joint Conference on Neural Networks (IJCNN'99) |
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City | Washington, DC, USA |
Period | 7/10/99 → 7/16/99 |
ASJC Scopus subject areas
- Software
- Artificial Intelligence