Speaker Tracking for Hands-Free Continuous Speech Recognition in Noise Based on a Spectrum-Entropy Beamforming Method

George NOKAS; Evangelos DERMATAS

Speaker Tracking for Hands-Free Continuous Speech Recognition in Noise Based on a Spectrum-Entropy Beamforming Method

George NOKAS, Evangelos DERMATAS

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In this paper, we present a novel beam-former capable of tracking a rapidly moving speaker in a very noisy environment. The localization algorithm extracts a set of candidate direction-of-arrival (DOA) for the signal sources using array signal processing methods in the frequency domain. A minimum variance (MV) beam-former identifies the speech signal DOA in the direction where the signal's spectrum entropy is minimized. A fine tuning process detects the MV direction which is closest to the initial estimation using a smaller analysis window. Extended experiments, carried out in the range of 20-0 dB SNR, show significant improvement in the recognition rate of a moving speaker especially in very low SNRs (from 11.11% to 43.79% at 0 dB SNR in anechoic environment and from 9.9% to 30.51% in reverberant environment).

Publication: IEICE TRANSACTIONS on Information Vol.E86-D No.4 pp.755-758

Publication Date: 2003/04/01

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Type of Manuscript: LETTER

Category: Speech and Hearing

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George NOKAS, Evangelos DERMATAS, "Speaker Tracking for Hands-Free Continuous Speech Recognition in Noise Based on a Spectrum-Entropy Beamforming Method" in IEICE TRANSACTIONS on Information, vol. E86-D, no. 4, pp. 755-758, April 2003, doi: .
Abstract: In this paper, we present a novel beam-former capable of tracking a rapidly moving speaker in a very noisy environment. The localization algorithm extracts a set of candidate direction-of-arrival (DOA) for the signal sources using array signal processing methods in the frequency domain. A minimum variance (MV) beam-former identifies the speech signal DOA in the direction where the signal's spectrum entropy is minimized. A fine tuning process detects the MV direction which is closest to the initial estimation using a smaller analysis window. Extended experiments, carried out in the range of 20-0 dB SNR, show significant improvement in the recognition rate of a moving speaker especially in very low SNRs (from 11.11% to 43.79% at 0 dB SNR in anechoic environment and from 9.9% to 30.51% in reverberant environment).
URL: https://global.ieice.org/en_transactions/information/10.1587/e86-d_4_755/_p

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@ARTICLE{e86-d_4_755,
author={George NOKAS, Evangelos DERMATAS, },
journal={IEICE TRANSACTIONS on Information},
title={Speaker Tracking for Hands-Free Continuous Speech Recognition in Noise Based on a Spectrum-Entropy Beamforming Method},
year={2003},
volume={E86-D},
number={4},
pages={755-758},
abstract={In this paper, we present a novel beam-former capable of tracking a rapidly moving speaker in a very noisy environment. The localization algorithm extracts a set of candidate direction-of-arrival (DOA) for the signal sources using array signal processing methods in the frequency domain. A minimum variance (MV) beam-former identifies the speech signal DOA in the direction where the signal's spectrum entropy is minimized. A fine tuning process detects the MV direction which is closest to the initial estimation using a smaller analysis window. Extended experiments, carried out in the range of 20-0 dB SNR, show significant improvement in the recognition rate of a moving speaker especially in very low SNRs (from 11.11% to 43.79% at 0 dB SNR in anechoic environment and from 9.9% to 30.51% in reverberant environment).},
keywords={},
doi={},
ISSN={},
month={April},}

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TY - JOUR
TI - Speaker Tracking for Hands-Free Continuous Speech Recognition in Noise Based on a Spectrum-Entropy Beamforming Method
T2 - IEICE TRANSACTIONS on Information
SP - 755
EP - 758
AU - George NOKAS
AU - Evangelos DERMATAS
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E86-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2003
AB - In this paper, we present a novel beam-former capable of tracking a rapidly moving speaker in a very noisy environment. The localization algorithm extracts a set of candidate direction-of-arrival (DOA) for the signal sources using array signal processing methods in the frequency domain. A minimum variance (MV) beam-former identifies the speech signal DOA in the direction where the signal's spectrum entropy is minimized. A fine tuning process detects the MV direction which is closest to the initial estimation using a smaller analysis window. Extended experiments, carried out in the range of 20-0 dB SNR, show significant improvement in the recognition rate of a moving speaker especially in very low SNRs (from 11.11% to 43.79% at 0 dB SNR in anechoic environment and from 9.9% to 30.51% in reverberant environment).
ER -