Noise subspace fuzzy C-means clustering for robust speech recognition

  • Authors:

  • J. M. Górriz;J. Ramírez;J. C. Segura;C. G. Puntonet;J. J. González

  • Affiliations:

  • Dpt. Signal Theory, Networking and communications, University of Granada, Spain;Dpt. Signal Theory, Networking and communications, University of Granada, Spain;Dpt. Signal Theory, Networking and communications, University of Granada, Spain;Dpt. Computer Architecture and Technology, University of Granada, Spain;Dpt. Computer Architecture and Technology, University of Granada, Spain

  • Venue:
  • ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part V
  • Year:
  • 2006

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Abstract

In this paper a fuzzy C-means (FCM) based approach for speech/non-speech discrimination is developed to build an effective voice activity detection (VAD) algorithm. The proposed VAD method is based on a soft-decision clustering approach built over a ratio of subband energies that improves recognition performance in noisy environments. The accuracy of the FCM-VAD algorithm lies in the use of a decision function defined over a multiple-observation (MO) window of averaged subband energy ratio and the modeling of noise subspace into fuzzy prototypes. In addition, time efficiency is also reached due to the clustering approach which is fundamental in VAD real time applications, i.e. speech recognition. An exhaustive analysis on the Spanish SpeechDat-Car databases is conducted in order to assess the performance of the proposed method and to compare it to existing standard VAD methods. The results show improvements in detection accuracy over standard VADs and a representative set of recently reported VAD algorithms.