Your browser does not support JavaScript!

Robust voice activity detection algorithm for estimating noise spectrum

Robust voice activity detection algorithm for estimating noise spectrum

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

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

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
Your details
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.

A new voice activity detection(VAD) algorithm is proposed for estimating the spectrum of car noise in which noise is filtered out in the frequency domain. The proposed algorithm uses the log energy parameters which are composed of two parts in the critical band. The algorithm detects the noise period by applying two adaptive thresholds to each part. Using the noise period we can reliably estimate the time-varying noise characteristics. The advantage of the proposed technique is that it can prevent incorrect detections caused by unvoiced or nasal sounds with high frequency components being covered by car noise with low frequency components. The algorithm is suitable for real time implementation with one microphone. Also, a speaker-independent speech recognition system has been implemented for car navigation using a fixed point Oak DSP system, which incorporates the proposed VAD algorithm. The system enhanced the recognition rates for 12 isolated command words to 94.52% compared with the 80.7% of the baseline recogniser.


    1. 1)
      • Scalart, P., Filho, J.V.: `Speech enhancement based on a priori signal to noise estimation', Proc. ICASSP, 1996, 2, p. 629–632.
    2. 2)
      • Shozakai, M., Nakamura, S., Shikano, K.: `Robust speech recognition in car environments', Proc. ICASSP, 1998, 1, p. 269–272.
    3. 3)
      • Yang, J.: `Frequency domain noise suppression approaches in mobile telephone systems', Proc. ICASSP, 1993, 2, p. 363–366.
    4. 4)
      • Viikki, O., Bye, D., Lauria, K.: `A recursive feature vector normalization approach for robust speech recognitionin noise', Proc. ICASSP, 1998, 2, p. 733–736.
    5. 5)
      • Y. Gong . Speech recognition in noisy environment : A survey. Speech Commun. , 3 , 261 - 291
    6. 6)
      • Pollak, P., Sovka, P., Uhlir, J.: `Cepstral speech/pause detectors', IEEE Workshop on Nonlinear Signal and Image Processing, 1995.
    7. 7)
      • R.L. Bopuquin-Jeannes , G. Faucon . Study of a voice activity detector and its influence on a noise reductionsystem. Speech Commun. , 3 , 245 - 254
    8. 8)
      • OakDSPCore™ Architecture Specification.
    9. 9)
      • Gerven, S.V., Xie, F.: `A comparative study of speech detection methods', Proc. EUROSPEECH, 1997, 3, p. 1095–1098.
    10. 10)
      • S.F. Boll . Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans. , 2 , 113 - 120
    11. 11)
      • L.R. Rabiner , M.R. Sambur . An algorithm for determining the endpoint of isolated utterance. Bell Syst. Tech. J. , 2 , 297 - 315

Related content

This is a required field
Please enter a valid email address