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Engineering - Signals & Communication | Hierarchical Neural Network Structures for Phoneme Recognition

Hierarchical Neural Network Structures for Phoneme Recognition

Vasquez, Daniel, Gruhn, Rainer, Minker, Wolfgang

2013, XVIII, 134 p.

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  • Simplifies the analysis in spoken language dialogue systems
  • Investigates hierarchical structures based on neural networks for automatic speech recognition
  • Written for academic and industrial researchers in speech recognition

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.

Content Level » Research

Keywords » Artificial Neural Network - HMM/ANN - Hybrid Hidden Markov Model - Multilayered Perceptron MLP - TIMIT database - articulatory attributes - phoneme recognition - phonetic decoder - phonotactics - spoken language dialogue systems

Related subjects » Artificial Intelligence - Computational Intelligence and Complexity - HCI - Signals & Communication

Table of contents 

Background in Speech Recognition.- Phoneme Recognition Task.- Hierarchical Approach and Downsampling Schemes.- Extending the Hierarchical Scheme: Inter and Intra Phonetic Information.- Theoretical framework for phoneme recognition analysis.

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