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It is known that the perceived loudness of a tone signal by a human is spectrally masked by background noise. This masking effect causes not only a shift of just-audible sound pressure level of the tone, but also produces a masked loudness function having steeper slope than the unmasked one. This masking property of perceived loudness stimulates us to(More)
Recommended by Douglas O'Shaughnessy This paper presents a novel model compensation (MC) method for the features of mel-frequency cepstral coefficients (MFCCs) with signal-to-noise-ratio-(SNR-) dependent nonuniform spectral compression (SNSC). Though these new MFCCs derived from a SNSC scheme have been shown to be robust features under matched case, they(More)
Model-based compensation techniques have been successfully used for speech recognition in noisy environments. Popular model-based compensation methods such as the Log-Normal PMC and Log-Add PMC generally use approximate compensation for dynamic parameters. Hence their recognition accuracy is degraded at low and very low signal-to-noise ratios. In this paper(More)
This paper proposes a novel model compensation method for a robust feature extraction technique based on SNR-dependent nonuniform spectral compression (SNSC). The SNSC method is a spectral transformation which resembles human's intensity-to-loudness conversion and de-emphasizes the contributions from noisy spectral components to features. In this paper, we(More)
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