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A Hybrid Approach to Pattern Matching for Text-to-Speech Conversion

  • Chew Lim Tan
  • Yan Rong Chen
  • Paul Hong Jyh Wu
Conference paper

Abstract

Assignment of phonetic symbols to characters in a text-to-speech conversion system is a pattern analysis and recognition process. This research proposes a hybrid approach to pattern matching for speech synthesis by machine. The problem statement may be reduced as follows: How do we assign a phoneme to a character given its contextual information, i.e. the characters preceding and following the character? In our present study, we use a contextual window of five characters wide allowing up to two characters on either side of each character in question for phoneme assignment. The assignment method is based on a machine learning approach by training the system with a large set of examples. The hybrid approach is to integrate an information gain learning algorithm with a transformation-based error driven learning algorithm. The examples for training and testing in the present work are taken from NETtalk Corpus, containing a list of 20,008 English words along with a phonetic transcription for each word. This hybrid approach has been shown to achieve a final accuracy of 96.86%.

Keywords

Hybrid Approach Information Gain Testing Pattern Transformation Rule Speech Synthesis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 1999

Authors and Affiliations

  • Chew Lim Tan
    • 1
  • Yan Rong Chen
    • 1
  • Paul Hong Jyh Wu
    • 2
  1. 1.School of ComputingNational University of SingaporeKent RidgeSingapore
  2. 2.Kent Ridge Digital LabsKent RidgeSingapore

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