Noise Robust Front-end for ASR using Spectral Subtraction, Spectral Flooring and Cumulative Distribution Mapping
نویسنده
چکیده
In this paper, a novel and noise robust front-end based on the combined application of spectral subtraction, spectral flooring and cumulative distribution mapping is proposed. Recognition experiments with the Aurora II connected digits reveal that the proposed front-end achieves an average digit accuracy of 81.46% for a model set trained from clean data and 89.54% for a model set trained from data with various noise conditions. With reference to the ETSI standard Mel-cepstral front-end, the proposed front-end obtains a relative error reduction of around 52% for the clean model set and 14% for the multi-condition model set. Moreover, it is observed that the use of a single fixed parameter to control spectral flooring is beneficial only when cumulative distribution mapping is also applied at a later stage of the frontend processing.
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