Modulation-based detection of speech in real background noise: Generalization to novel background classes

Robust detection of speech embedded in real acoustic background noise is considered using an approach based on subband amplitude modulation spectral (AMS) features and trained discriminative classifiers. Performance is evaluated in particular for situations in which speech is embedded in acoustic ba...

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Bibliographic Details
Main Authors: Bach, Jorg-Hendrik, Kollmeier, Birger, Anemüller, Jörn
Format: Conference Proceeding
Language:English
Subjects:
Acoustic signal detection
Amplitude modulation
Background noise
Feature extraction
Frequency modulation
pattern classification
Physics
Spectrogram
Speech enhancement
speech processing
Support vector machine classification
Support vector machines
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Summary:Robust detection of speech embedded in real acoustic background noise is considered using an approach based on subband amplitude modulation spectral (AMS) features and trained discriminative classifiers. Performance is evaluated in particular for situations in which speech is embedded in acoustic backgrounds not presented during classifier training, and for signal-to-noise ratios (SNR) from -10 dB to 20 dB. The results show that (1) Generalization to novel background classes with AMS features yields better performance in 84% of investigated situations, corresponding to an SNR benefit of about 10 dB compared to mel-frequency cepstral coefficient (MFCC) features. (2) On known backgrounds, AMS and MFCCs achieve similar performance, with a small advantage for AMS in negative SNR regimes. (3) Standard voice activity detection (ITU G729.B) performs significantly worse than the classification-based approach.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2010.5496244