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Titlebook: Robust Adaptation to Non-Native Accents in Automatic Speech Recognition; Silke Goronzy Book 2002 Springer-Verlag Berlin Heidelberg 2002 Au

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书目名称Robust Adaptation to Non-Native Accents in Automatic Speech Recognition
编辑Silke Goronzy
视频video
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Robust Adaptation to Non-Native Accents in Automatic Speech Recognition;  Silke Goronzy Book 2002 Springer-Verlag Berlin Heidelberg 2002 Au
描述.Speech 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..
出版日期Book 2002
关键词Automat; Automatic Speech Recognition; Confidence Measures; Human-Machine Interfaces; MLLR; Natural Langu
版次1
doihttps://doi.org/10.1007/3-540-36290-8
isbn_softcover978-3-540-00325-0
isbn_ebook978-3-540-36290-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2002
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Pre-processing of the Speech Data,rst,by using a microphone,the speech signal or speech waveform is converted into an electrical signal. Figure 3.1 shows a block diagram of a typical speech analysis procedure, generating . coefficients (MFCCs).The different steps are explained in the following sections.
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Speaker Adaptation,rates than SI ones. If provided with the same amount of training data, they can achieve an average word error rate a factor of two or three lower than the SI system [Woo99]. But to train SD systems, large amounts of speaker specific speech data are needed and it is often not feasible to collect this
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Pronunciation Adaptation,native data was proposed. It was shown that performance decreases if a recogniser that is optimised for native speech is exposed to non-native speech. Investigations on the ISLE database showed that including specialised non- native pronunciation variants in the dictionary can greatly improve the re
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Phoneme Inventory,les (orthography and SAMPA transcription). Since some of the English phonemes do not exist in German a mapping to the phonetically closest German phoneme was necessary for some experiments.This mapping is given in the fourth coulmn in SectionC.2
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