Statistical Pronunciation Modeling for Non-Native Speech by Rainer E. Gruhn, Wolfgang Minker, Satoshi Nakamura PDF

By Rainer E. Gruhn, Wolfgang Minker, Satoshi Nakamura

ISBN-10: 3642195857

ISBN-13: 9783642195853

In this paintings, the authors current an absolutely statistical method of version non--native audio system' pronunciation. Second-language audio system pronounce phrases in a number of alternative ways in comparison to the local audio system. these deviations, may possibly it's phoneme substitutions, deletions or insertions, could be modelled instantly with the recent process awarded here.
The equipment relies on a discrete hidden Markov version as a notice pronunciation version, initialized on a regular pronunciation dictionary. The implementation and performance of the technique has been confirmed and validated with a try set of non-native English within the relating to accent.
The booklet is written for researchers with a qualified curiosity in phonetics and automated speech and speaker recognition.

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Statistical Pronunciation Modeling for Non-Native Speech by Rainer E. Gruhn, Wolfgang Minker, Satoshi Nakamura PDF

During this paintings, the authors current a completely statistical method of version non--native audio system' pronunciation. Second-language audio system pronounce phrases in a number of other ways in comparison to the local audio system. these deviations, might or not it's phoneme substitutions, deletions or insertions, will be modelled instantly with the recent process offered the following.

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Extra info for Statistical Pronunciation Modeling for Non-Native Speech Processing

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2 Difference matrix comparing native speakers of English with Japanese accented English. The darker, the greater the difference speakers, but less frequently so. The reason is that the German language does not have a /Z/ sound, whereas Japanese has both /S/ and /Z/. Japanese speakers of English have problems producing the /R/ sound, the phoneme recognizer classifies it as /L/ or a wide range of other phonemes. Again the reason is that the Japanese language has no /R/. While the graphs also contain some random confusions, it is still visible that there are some accent-specific confusion patterns.

The best way to classify non-native speech databases is regarding to the type of application they are designed for. The major fields are navigation devices or travel assistance, military communications, presentation systems or computer assisted language learning systems. 1 Speech Operated Travel Assistance A possible future application of non-native speech recognizers are automatic tourist information or hotel booking systems. As they are unlikely to cover any language in the world, to interact with the system many travelers will have to speak in English—non-native English.

Furthermore, one of the typical demonstration sample dialogs, ‘‘demo02’’, was also added. A transcription sample from the hotel reservation dialogs can be found in Appendix A. Two of the hotel reservation dialogs, TAS22001 and TAS3202, were defined as test set of about three minutes, the rest of about eleven minutes as training data. The sentence set was chosen based on which data is helpful for non-native speech research. These are some phonetically compact sentences for good phonetic coverage, and some sentences from the target scenario, which is hotel reservation.

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Statistical Pronunciation Modeling for Non-Native Speech Processing by Rainer E. Gruhn, Wolfgang Minker, Satoshi Nakamura


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