Amazon cover image
Image from Amazon.com

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

By: Contributor(s): Series: Signals and Communication TechnologyPublisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Description: XII, 172 p. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783642198991
Subject(s): Genre/Form: Additional physical formats: Printed edition:: No titleDDC classification:
  • 621.382 23
LOC classification:
  • TK5102.9
  • TA1637-1638
  • TK7882.S65
Online resources:
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.
Item type: eBooks
Star ratings
    Average rating: 0.0 (0 votes)
No physical items for this record

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.

Copyright © 2020 Alfaisal University Library. All Rights Reserved.
Tel: +966 11 2158948 Fax: +966 11 2157910 Email:
librarian@alfaisal.edu