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In this paper, we introduce a new concept in advancing the noise robustness of speech recognition front-end. The presented method, called SNR-dependent Waveform Processing (SWP), exploits SNR variability within a speech period for enhancing the high SNR period portion and attenuating the low SNR period portion in the waveform time domain. In this way, the(More)
We propose a novel method of efficiently searching very large populations of speakers, tens of thousands or more, using an utterance comparison model proposed in a previous work. The model allows much more efficient comparison of utterances compared to the traditional Gaussian Mixture Model(GMM)-based approach because of its computational simplicity while(More)
This paper presents the robust front-end algorithm that was submitted by Motorola to the ETSI STQ-Aurora DSR working group as a proposal for the Advanced DSR front-end in January 2001. The algorithm consists of a two-stage mel-warped Wiener filter, a waveform processor, a channel-normalized mel-frequency cepstral calculation and a subsystem of post-cepstral(More)