| Paper: | SP-P15.9 | ||
| Session: | Robustness in Noisy Environments | ||
| Time: | Friday, May 21, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Speech Processing: Robust Speech Recognition | ||
| Title: | SNR-DEPENDENT NON-UNIFORM SPECTRAL COMPRESSION FOR NOISY SPEECH RECOGNITION | ||
| Authors: | Kam-keung Chu; City University of Hong Kong | ||
| Shu Hung Leung; City University of Hong Kong | |||
| Abstract: | It is known that the perceived loudness of a tone signal by human is spectrally masked by background noises. 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 propose a new mel-scale-based feature extraction method with non-uniform spectral compression for speech recognition in noisy environments. In this method, the speech power spectrum is to undergo a mel-scaled band-pass filtering, as in standard MFCC front-end. However, the energies of the outputs of the filters are compressed by different root values defined by a compression function. This compression function is a function of the SNR in each filter band. Using this new scheme of SNR-dependent non-uniform spectral compression (SNSC) for mel-scaled filter-bank-based cepstral coefficients, substantial improvement is found for recognition in different noisy environments, as compared to the standard MFCC and features derived with cubic root spectral compression. | ||
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