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Abstract
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| This presents a robust voice activity detector (V AD) based on Hidden Markov Models (HMM) in stationary and non-stationary noise environments: inside motor vehicles (like cars or planes) or inside buDdings close to high traffic places (like in a control tower for air tramc control (ATC». In these environments, there is a high stationary noise level caused by vehicle motors and additionally, there could be people speaking at certain distance from the main speaker producing non-stationary noise. The V AD presented herein Is characterized by a new front-end and a noise level adaptation process that increases significantly the V AD robustness for different signal to noise ratios (SNRs). The feature vector used by the V AD includes the most relevant Mel Frequency Cepstral Coefficients (MFCC), normalized log energy, and delta log energy. The proposed V AD has been evaluated and compared to other weD-known V ADs using three databases containing different noise conditions: speech in clean environments (SNRs > 20 dB), speech recorded in stationary noise environments (inside or close to motor vehicles), and finally, speech in non-stationary environments (including noise from bars, television, and far-field speakers). In the three cases, the detection error obtained with the proposed V AD is the lowest for all SNRs compared to Acero's V AD | |
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International
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Si |
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JCR
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Si |
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Title
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Ieee Aerospace And Electronic Systems Magazine |
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ISBN
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0885-8985 |
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Impact factor JCR
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0,179 |
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Impact info
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Volume
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26 |
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10.1109/MAES.2011.6070277 |
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Journal number
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11 |
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From page
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16 |
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To page
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23 |
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Month
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NOVIEMBRE |
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Ranking
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Q4 |