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Self-Learning Speaker Identification [electronic resource] :A System for Enhanced Speech Recognition / by Tobias Herbig, Franz Gerl, Wolfgang Minker.

by Herbig, Tobias [author.]; Gerl, Franz [author.]; Minker, Wolfgang [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Signals and Communication Technology: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011.Description: XII, 172 p. online resource.ISBN: 9783642198991.Subject(s): Engineering | Computer science | Biometrics | Telecommunication | Engineering | Signal, Image and Speech Processing | Biometrics | Communications Engineering, Networks | User Interfaces and Human Computer InteractionDDC classification: 621.382 Online resources: Click here to access online
Contents:
Introduction -- State of the Art -- Fundamentals -- Speech Production -- Front-End -- Speaker Change -- Speaker Identification.-Speaker Adaptation.
In: Springer eBooksSummary: Current speech recognition systems suffer from variation of voice characteristics between speakers as they are usually based on speaker independent speech models. In order to resolve this issue, adaptation methods have been developed in many state-of-the-art systems. However, information acquired over time is still lost whenever another speaker intermittently uses the recognition system. This work therefore develops an integrated approach for speech and speaker recognition in order to improve the self-learning opportunities of the system. A speaker adaptation scheme is introduced. It is suited for fast short-term and detailed long-term adaptation. These adaptation profiles are then used for an efficient speaker recognition system. The speaker identification enables the speaker adaptation to track different speakers which results in an optimal long-term adaptation.
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Item type Current location Call number Status Date due Barcode
TA1637-1638 (Browse shelf) Available
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Long Loan MAIN LIBRARY
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Introduction -- State of the Art -- Fundamentals -- Speech Production -- Front-End -- Speaker Change -- Speaker Identification.-Speaker Adaptation.

Current speech recognition systems suffer from variation of voice characteristics between speakers as they are usually based on speaker independent speech models. In order to resolve this issue, adaptation methods have been developed in many state-of-the-art systems. However, information acquired over time is still lost whenever another speaker intermittently uses the recognition system. This work therefore develops an integrated approach for speech and speaker recognition in order to improve the self-learning opportunities of the system. A speaker adaptation scheme is introduced. It is suited for fast short-term and detailed long-term adaptation. These adaptation profiles are then used for an efficient speaker recognition system. The speaker identification enables the speaker adaptation to track different speakers which results in an optimal long-term adaptation.

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