IEICE TRANSACTIONS on Information
Voice Activity Detection Based on Generalized Normal-Laplace Distribution Incorporating Conditional MAP
Summary :
In this paper, we propose a novel voice activity detection (VAD) algorithm based on the generalized normal-Laplace (GNL) distribution to provide enhanced performance in adverse noise environments. Specifically, the probability density function (PDF) of a noisy speech signal is represented by the GNL distribution; the variance of the speech and noise of the GNL distribution are estimated using higher-order moments. After in-depth analysis of estimated variances, a feature that is useful for discrimination between speech and noise at low SNRs is derived and compared to a threshold to detect speech activity. To consider the inter-frame correlation of speech activity, the result from the previous frame is employed in the decision rule of the proposed VAD algorithm. The performance of our proposed VAD algorithm is evaluated in terms of receiver operating characteristics (ROC) and detection accuracy. Results show that the proposed method yields better results than conventional VAD algorithms.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.12 pp.2888-2891
- Publication Date
- 2013/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.2888
- Type of Manuscript
- LETTER
- Category
- Speech and Hearing
Authors
Ji-Hyun SONG
Inha University
Sangmin LEE
Inha University
Keyword
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Ji-Hyun SONG, Sangmin LEE, "Voice Activity Detection Based on Generalized Normal-Laplace Distribution Incorporating Conditional MAP" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 12, pp. 2888-2891, December 2013, doi: 10.1587/transinf.E96.D.2888.
Abstract: In this paper, we propose a novel voice activity detection (VAD) algorithm based on the generalized normal-Laplace (GNL) distribution to provide enhanced performance in adverse noise environments. Specifically, the probability density function (PDF) of a noisy speech signal is represented by the GNL distribution; the variance of the speech and noise of the GNL distribution are estimated using higher-order moments. After in-depth analysis of estimated variances, a feature that is useful for discrimination between speech and noise at low SNRs is derived and compared to a threshold to detect speech activity. To consider the inter-frame correlation of speech activity, the result from the previous frame is employed in the decision rule of the proposed VAD algorithm. The performance of our proposed VAD algorithm is evaluated in terms of receiver operating characteristics (ROC) and detection accuracy. Results show that the proposed method yields better results than conventional VAD algorithms.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.2888/_p
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@ARTICLE{e96-d_12_2888,
author={Ji-Hyun SONG, Sangmin LEE, },
journal={IEICE TRANSACTIONS on Information},
title={Voice Activity Detection Based on Generalized Normal-Laplace Distribution Incorporating Conditional MAP},
year={2013},
volume={E96-D},
number={12},
pages={2888-2891},
abstract={In this paper, we propose a novel voice activity detection (VAD) algorithm based on the generalized normal-Laplace (GNL) distribution to provide enhanced performance in adverse noise environments. Specifically, the probability density function (PDF) of a noisy speech signal is represented by the GNL distribution; the variance of the speech and noise of the GNL distribution are estimated using higher-order moments. After in-depth analysis of estimated variances, a feature that is useful for discrimination between speech and noise at low SNRs is derived and compared to a threshold to detect speech activity. To consider the inter-frame correlation of speech activity, the result from the previous frame is employed in the decision rule of the proposed VAD algorithm. The performance of our proposed VAD algorithm is evaluated in terms of receiver operating characteristics (ROC) and detection accuracy. Results show that the proposed method yields better results than conventional VAD algorithms.},
keywords={},
doi={10.1587/transinf.E96.D.2888},
ISSN={1745-1361},
month={December},}
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TY - JOUR
TI - Voice Activity Detection Based on Generalized Normal-Laplace Distribution Incorporating Conditional MAP
T2 - IEICE TRANSACTIONS on Information
SP - 2888
EP - 2891
AU - Ji-Hyun SONG
AU - Sangmin LEE
PY - 2013
DO - 10.1587/transinf.E96.D.2888
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2013
AB - In this paper, we propose a novel voice activity detection (VAD) algorithm based on the generalized normal-Laplace (GNL) distribution to provide enhanced performance in adverse noise environments. Specifically, the probability density function (PDF) of a noisy speech signal is represented by the GNL distribution; the variance of the speech and noise of the GNL distribution are estimated using higher-order moments. After in-depth analysis of estimated variances, a feature that is useful for discrimination between speech and noise at low SNRs is derived and compared to a threshold to detect speech activity. To consider the inter-frame correlation of speech activity, the result from the previous frame is employed in the decision rule of the proposed VAD algorithm. The performance of our proposed VAD algorithm is evaluated in terms of receiver operating characteristics (ROC) and detection accuracy. Results show that the proposed method yields better results than conventional VAD algorithms.
ER -
English
