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Modulation spectrum exponential weighting for robust speech recognition
In this paper, we present a novel scheme to improve the noise robustness of features in speech recognition for vehicle noise environments. In the algorithm termed modulation spectrum exponential weighting (MSEW), the magnitude spectra of feature streams are updated by integrating a reference magnitude spectrum and the original magnitude spectrum with varying exponential weights based on the signal-to-noise ratio (SNR) of the operating environment. Specifically, we present three modes of MSEW, which can be viewed as a generalization of the two algorithms, modulation spectrum replacement/filtering (MSR/MSF). In experiments conducted on the AURORA-2 noisy digit database, the presented MSEW algorithms can achieve better recognition accuracy rates relative to the original MSR and MSF in various vehicle-noise environments.
Modulation spectrum exponential weighting for robust speech recognition
In this paper, we present a novel scheme to improve the noise robustness of features in speech recognition for vehicle noise environments. In the algorithm termed modulation spectrum exponential weighting (MSEW), the magnitude spectra of feature streams are updated by integrating a reference magnitude spectrum and the original magnitude spectrum with varying exponential weights based on the signal-to-noise ratio (SNR) of the operating environment. Specifically, we present three modes of MSEW, which can be viewed as a generalization of the two algorithms, modulation spectrum replacement/filtering (MSR/MSF). In experiments conducted on the AURORA-2 noisy digit database, the presented MSEW algorithms can achieve better recognition accuracy rates relative to the original MSR and MSF in various vehicle-noise environments.
Modulation spectrum exponential weighting for robust speech recognition
Hao-teng Fan, (author) / Yi-cheng Lian, (author) / Jeih-weih Hung, (author)
2012-11-01
340459 byte
Conference paper
Electronic Resource
English
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