Hierarchical Neural Network Structures for Phoneme Recognition - Info and Reading Options
By Daniel Vasquez
“Hierarchical Neural Network Structures for Phoneme Recognition” Metadata:
- Title: ➤ Hierarchical Neural Network Structures for Phoneme Recognition
- Author: Daniel Vasquez
“Hierarchical Neural Network Structures for Phoneme Recognition” Subjects and Themes:
- Subjects: ➤ Engineering - Translators (Computer programs) - Language Translation and Linguistics - Image and Speech Processing Signal - User Interfaces and Human Computer Interaction - Computational intelligence - Computer science - Phonemics - Word recognition - Automatic speech recognition - Signal processing, digital techniques
Edition Identifiers:
- The Open Library ID: OL19855221W
AI-generated Review of “Hierarchical Neural Network Structures for Phoneme Recognition”:
"Hierarchical Neural Network Structures for Phoneme Recognition" Description:
The Open Library:
<p>In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are evaluated on the phoneme recognition task where a Hybrid Hidden Markov Model/Artificial Neural Network paradigm is used. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron. Additionally, the output of the first level serves as a second level input. The computational speed of the phoneme recognizer can be substantially increased by removing redundant information still contained at the first level output. Several techniques based on temporal and phonetic criteria have been investigated to remove this redundant information. The computational time could be reduced by 57% whilst keeping the system accuracy comparable to the baseline hierarchical approach.</p>
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