000 | 03288nam a22006015i 4500 | ||
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001 | 978-3-540-36290-6 | ||
003 | DE-He213 | ||
005 | 20240423132555.0 | ||
007 | cr nn 008mamaa | ||
008 | 121227s2002 gw | s |||| 0|eng d | ||
020 |
_a9783540362906 _9978-3-540-36290-6 |
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024 | 7 |
_a10.1007/3-540-36290-8 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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_aUYQ _2thema |
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082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aGoronzy, Silke. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aRobust Adaptation to Non-Native Accents in Automatic Speech Recognition _h[electronic resource] / _cby Silke Goronzy. |
250 | _a1st ed. 2002. | ||
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2002. |
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300 |
_aXI, 146 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v2560 |
|
505 | 0 | _aASR:AnOverview -- Pre-processing of the Speech Data -- Stochastic Modelling of Speech -- Knowledge Bases of an ASR System -- Speaker Adaptation -- Confidence Measures -- Pronunciation Adaptation -- Future Work -- Summary -- Databases and Experimental Settings -- MLLR Results -- Phoneme Inventory. | |
520 | _aSpeech recognition technology is being increasingly employed in human-machine interfaces. A remaining problem however is the robustness of this technology to non-native accents, which still cause considerable difficulties for current systems. In this book, methods to overcome this problem are described. A speaker adaptation algorithm that is capable of adapting to the current speaker with just a few words of speaker-specific data based on the MLLR principle is developed and combined with confidence measures that focus on phone durations as well as on acoustic features. Furthermore, a specific pronunciation modelling technique that allows the automatic derivation of non-native pronunciations without using non-native data is described and combined with the previous techniques to produce a robust adaptation to non-native accents in an automatic speech recognition system. | ||
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aSignal processing. | |
650 | 0 | _aMachine theory. | |
650 | 0 | _aUser interfaces (Computer systems). | |
650 | 0 | _aHuman-computer interaction. | |
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aSignal, Speech and Image Processing. |
650 | 2 | 4 | _aFormal Languages and Automata Theory. |
650 | 2 | 4 | _aUser Interfaces and Human Computer Interaction. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783540003250 |
776 | 0 | 8 |
_iPrinted edition: _z9783662205549 |
830 | 0 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v2560 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/3-540-36290-8 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
912 | _aZDB-2-LNC | ||
912 | _aZDB-2-BAE | ||
942 | _cSPRINGER | ||
999 |
_c189284 _d189284 |