Yan Ming Cheng

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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)
We view the speech enhancement task in two aspects: reduction of the perceptual noise level in degraded speech and reconstruction of the degraded information, which may result in improvement of speech intelligibility. We are also very interested in noiseindependent speech enhancement where test noise environments could differ in intensity from those of(More)