EUROSPEECH 2001 Scandinavia
In this paper, the procedure for feature vector extraction in multiconditionally noisy environments is presented. Proposed front-end uses time and spectral domain processing for noise reduction as well as feature extraction to create mel-cepstrum parameters and achieves a trade-off between effective noise reduction and low computational load for real-time operations. First, a novel weighting function is used to reduce the rough noise in time domain, and then a spectral subtraction method based on minimum statistics is applied to decrease the effect of additive broadband noise on speech in the spectral domain. At final stage, a feature vector, which consists of 12 mel-cepstrum parameters and the energy, is created. For evaluation of improvement of speech recognition with presented front-end, the "Aurora 2" database together with the HTK recognition toolkit have been chosen. With proposed method an average improvement in performance of 24.75% relative to the current ETSI Aurora standard was achieved.
Bibliographic reference. Kotnik, Bojan / Kacic, Zdravko / Horvat, Bogomir (2001): "A multiconditional robust front-end feature extraction with a noise reduction procedure based on improved spectral subtraction algorithm", In EUROSPEECH-2001, 197-200.