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Record Nr. |
UNINA9910299821303321 |
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Autore |
Rao K. Sreenivasa (Krothapalli Sreenivasa) |
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Titolo |
Language Identification Using Excitation Source Features / / by K. Sreenivasa Rao, Dipanjan Nandi |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
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ISBN |
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Edizione |
[1st ed. 2015.] |
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Descrizione fisica |
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1 online resource (128 p.) |
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Collana |
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SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning, , 2191-737X |
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Disciplina |
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006.35 |
410.285 |
620 |
621.382 |
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Soggetti |
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Signal processing |
Image processing |
Speech processing systems |
Natural language processing (Computer science) |
Computational linguistics |
Signal, Image and Speech Processing |
Natural Language Processing (NLP) |
Computational Linguistics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di contenuto |
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Introduction -- Language Identification--A Brief Review -- Implicit Excitation Source Features for Language Identification -- Parametric Excitation Source Features for Language Identification -- Complementary and Robust Nature of Excitation Source Features for Language Identification -- Conclusion. |
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Sommario/riassunto |
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This book discusses the contribution of excitation source information in discriminating language. The authors focus on the excitation source component of speech for enhancement of language identification (LID) performance. Language specific features are extracted using two different modes: (i) Implicit processing of linear prediction (LP) residual |
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and (ii) Explicit parameterization of linear prediction residual. The book discusses how in implicit processing approach, excitation source features are derived from LP residual, Hilbert envelope (magnitude) of LP residual and Phase of LP residual; and in explicit parameterization approach, LP residual signal is processed in spectral domain to extract the relevant language specific features. The authors further extract source features from these modes, which are combined for enhancing the performance of LID systems. The proposed excitation source features are also investigated for LID in background noisy environments. Each chapter of this book provides the motivation for exploring the specific feature for LID task, and subsequently discuss the methods to extract those features and finally suggest appropriate models to capture the language specific knowledge from the proposed features. Finally, the book discuss about various combinations of spectral and source features, and the desired models to enhance the performance of LID systems. |
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