Noise Robust Feature Extraction for ASR using the Aurora 2 Database
2001
Four front-end processing techniques developed for noise robust speech recognition are tested with the Aurora 2 database. These techniques include three previously published algorithms: variable frame rate analysis [Zhu and Alwan, 2000], peak isolation [Strope and Alwan, 1997], and harmonic demodulation [Zhu and Alwan, 2000], and a new technique for peak-to-valley ratio locking. Our previous work has focused on isolated digit recognition. In this paper, these algorithms are modified for recognition of connected digits. Recognition results with the Aurora 2 database show that a combination of these four techniques results in 40% error rate reduction when compared to the baseline MFCC front-end for the clean training condition, with no significant increase in computational complexity.
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