Advertisement

© 2013

Hierarchical Neural Network Structures for Phoneme Recognition

  • 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

Book

Part of the Signals and Communication Technology book series (SCT)

Table of contents

  1. Front Matter
    Pages 1-15
  2. Daniel Vasquez, Rainer Gruhn, Wolfgang Minker
    Pages 1-6
  3. Daniel Vasquez, Rainer Gruhn, Wolfgang Minker
    Pages 7-30
  4. Daniel Vasquez, Rainer Gruhn, Wolfgang Minker
    Pages 31-48
  5. Daniel Vasquez, Rainer Gruhn, Wolfgang Minker
    Pages 49-59
  6. Daniel Vasquez, Rainer Gruhn, Wolfgang Minker
    Pages 61-101
  7. Daniel Vasquez, Rainer Gruhn, Wolfgang Minker
    Pages 103-117
  8. Daniel Vasquez, Rainer Gruhn, Wolfgang Minker
    Pages 119-122
  9. Back Matter
    Pages 0--1

About this book

Introduction

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.

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

Authors and affiliations

  1. 1., Institute of Information TechnologyUniversity of UlmUlmGermany
  2. 2.SVOX Deutschland GmbHUlmGermany
  3. 3., Institute of Information TechnologyUniversity of UlmUlmGermany

Bibliographic information

Industry Sectors
Pharma
Automotive
Biotechnology
Electronics
IT & Software
Telecommunications
Law
Aerospace
Oil, Gas & Geosciences
Engineering

Reviews

From the reviews:

“This brief book comes packed with useful information about some novel techniques for the recognition of speech building blocks known as phonemes. … it is brimming with useful and well-presented information. I recommend it for graduate students in the field, as well as for practicing professionals.” (Vladimir Botchev, Computing Reviews, May, 2013)