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Gaussian Mixture Clustering and Language Adaptation for the Development of a New Language Speech Recognition System.

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Title: Gaussian Mixture Clustering and Language Adaptation for the Development of a New Language Speech Recognition System.
Authors: Chatzichrisafis, Nikos1 Nikos.Chatzichrisafis@vozZup.com, Diakoloukas, Vassilios2 vas@ltelecom.tuc.gr, Digalakis, Vassilios2 vdiak@telecom.tuc.gr, Harizakis, Costas2 harizak@speech.gr
Source: IEEE Transactions on Audio, Speech & Language Processing. Mar2007, Vol. 15 Issue 3, p928-938. 11p. 4 Diagrams, 7 Charts, 7 Graphs.
Subjects: Speech processing systems, Application software porting, Speech perception, Language & languages, Acoustic models
Abstract: The porting of a speech recognition system to a new language is usually a time-consuming and expensive process since it requires collecting, transcribing, and processing a large amount of language-specific training sentences. This work presents techniques for improved cross-language transfer of speech recognition systems to new target languages. Such techniques are particularly useful for target languages where minimal amounts of training data are available. We describe a novel method to produce a language-independent system by combining acoustic models from a number of source languages. This intermediate language-independent acoustic model is used to bootstrap a target-language system by applying language adaptation. For our experiments, we use acoustic models of seven source languages to develop a target Greek acoustic model. We show that our technique significantly outperforms a system trained from scratch when less than 8 h of read speech is available. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Audio, Speech & Language Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: <searchLink fieldCode="DE" term="%22Speech+processing+systems%22">Speech processing systems</searchLink><br /><searchLink fieldCode="DE" term="%22Application+software+porting%22">Application software porting</searchLink><br /><searchLink fieldCode="DE" term="%22Speech+perception%22">Speech perception</searchLink><br /><searchLink fieldCode="DE" term="%22Language+%26+languages%22">Language & languages</searchLink><br /><searchLink fieldCode="DE" term="%22Acoustic+models%22">Acoustic models</searchLink>
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  Data: The porting of a speech recognition system to a new language is usually a time-consuming and expensive process since it requires collecting, transcribing, and processing a large amount of language-specific training sentences. This work presents techniques for improved cross-language transfer of speech recognition systems to new target languages. Such techniques are particularly useful for target languages where minimal amounts of training data are available. We describe a novel method to produce a language-independent system by combining acoustic models from a number of source languages. This intermediate language-independent acoustic model is used to bootstrap a target-language system by applying language adaptation. For our experiments, we use acoustic models of seven source languages to develop a target Greek acoustic model. We show that our technique significantly outperforms a system trained from scratch when less than 8 h of read speech is available. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Audio, Speech & Language Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/TASL.2006.885259
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        Type: general
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      – SubjectFull: Speech perception
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      – SubjectFull: Language & languages
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              Text: Mar2007
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