Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Genetic programming-based voice activity detection

Genetic programming-based voice activity detection

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© IEE
Published

A voice activity detection (VAD) algorithm is generated by using genetic programming (GP). The inputs of this VAD are the parameters extracted from the speech signals according to the ITU-T G.729B VAD standard. The GP-based VAD algorithm (GP-VAD) is evaluated using the AURORA-2 database. It is shown that the GP-VAD achieves approximately the same behaviour as the G.729B standard with a high artificial-to-intelligence ratio.

Inspec keywords: speech processing; artificial intelligence; standards; genetic algorithms

Other keywords: VAD algorithm; speech signal; AURORA-2 database; G.729B standard; ITU-T G.729B VAD standard; GP-VAD; high artificial-to-intelligence ratio; genetic programming; parameters extraction; voice activity detection; artificial-to-intelligence ratio

Subjects: Speech processing techniques; Optimisation techniques; Optimisation techniques; Speech and audio signal processing

References

    1. 1)
      • ETSI EN 301 708 recommendation, ‘Voice activity detector (VAD) for adaptive multi-rate (AMR) speech traffic channels’, 1999.
    2. 2)
      • J. Ramirez . Efficient voice activity detection algorithms using long-term speech information. Speech Commun. , 271 - 287
    3. 3)
    4. 4)
      • J.R. Koza . (2003) Genetic Programming IV. Routine human-competitive machine intelligence.
    5. 5)
      • Hirsch, H.G., Pearce, D.: `The AURORA experimental framework for the performance evaluation of speech recognition systems under noisy conditions', Proc. ISCA ITRW ASR: Automatic Speech Recognition: Challenges for the Next Millenium, 2000, Paris, France, p. 181–188.
    6. 6)
      • J.R. Koza . (1992) Genetic Programming: On the programming of computers by means of natural selection.
    7. 7)
    8. 8)
      • ETSI ES 202 050 recommendation, ‘Speech processing, transmission and quality aspects (STQ); distributed speech recognition; advanced front-end feature extraction algorithm; compression algorithms’, 2002.
    9. 9)
      • ITU-T Rec. G729, Annex B, ‘A silence compression scheme for G729 optimized for terminals conforming to ITU-T V.70’, 1996.
    10. 10)
    11. 11)
      • http://beagle.gel.ulaval.ca/index.html.
http://iet.metastore.ingenta.com/content/journals/10.1049/el_20052475
Loading

Related content

content/journals/10.1049/el_20052475
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address