Statistical Pronunciation Modeling for Non-Native Speech Processing [electronic resource] / by Rainer E. Gruhn, Wolfgang Minker, Satoshi Nakamura.

By: Gruhn, Rainer E [author.]Contributor(s): Minker, Wolfgang [author.] | Nakamura, Satoshi [author.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Signals and Communication Technology: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2011Description: X, 114 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783642195860Subject(s): Engineering | Translators (Computer programs) | Phonology | Engineering | Signal, Image and Speech Processing | Language Translation and Linguistics | Phonology | Statistics for Engineering, Physics, Computer Science, Chemistry and Earth SciencesAdditional physical formats: Printed edition:: No titleDDC classification: 621.382 LOC classification: TK5102.9TA1637-1638TK7882.S65Online resources: Click here to access online
Contents:
Introduction -- Automatic Speech Recognition -- Properties of Non-native Speech -- Pronunciation Variation Modeling in the Literature -- Non-native Speech Database -- Handling Non-native Speech -- Pronunciation HMMs.
In: Springer eBooksSummary: In this work, the authors present a fully statistical approach to model non--native speakers' pronunciation. Second-language speakers pronounce words in multiple different ways compared to the native speakers. Those deviations, may it be phoneme substitutions, deletions or insertions, can be modelled automatically with the new method presented here. The methods is based on a discrete hidden Markov model as a word pronunciation model, initialized on a standard pronunciation dictionary. The implementation and functionality of the methodology has been proven and verified with a test set of non-native English in the regarding accent. The book is written for researchers with a professional interest in phonetics and automatic speech and speaker recognition.
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Introduction -- Automatic Speech Recognition -- Properties of Non-native Speech -- Pronunciation Variation Modeling in the Literature -- Non-native Speech Database -- Handling Non-native Speech -- Pronunciation HMMs.

In this work, the authors present a fully statistical approach to model non--native speakers' pronunciation. Second-language speakers pronounce words in multiple different ways compared to the native speakers. Those deviations, may it be phoneme substitutions, deletions or insertions, can be modelled automatically with the new method presented here. The methods is based on a discrete hidden Markov model as a word pronunciation model, initialized on a standard pronunciation dictionary. The implementation and functionality of the methodology has been proven and verified with a test set of non-native English in the regarding accent. The book is written for researchers with a professional interest in phonetics and automatic speech and speaker recognition.

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