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In this paper, a two-stage multi-speaker identification (SID) system is proposed for mixed speeches with multiple speakers speaking simultaneously. By investigating the second stage processing, we improved the performance of multi-speaker SID from 94.6% to 99.0% on a standard testing set, and comparing with another state-of-art system, the proposed results were also a little better. We also examined...
In this paper, a multi-speaker identification system for co-channel speech is proposed. By using constrained likelihood and floating TMR method, this system can identify two speakers on co-channel speech with high accuracy.
Monaural speech separation is one of the most difficult problems in speech signal processing. In this paper, a new method based on machine learning and computational auditory scene analysis (CASA) is suggested to separate the monaural speech of two-talker. The technique of machine learning is used to learn the grouping cues on isolated clean data from single speaker. By using a factorial-max vector...
As an improvement to cepstral coefficients, an approach, called variance-based filtering model (VTM) of speech, is presented in this paper, in which the information of cepstral vectors is decomposed into intrinsic and noise components according to their variances. And then, contextual principal curves filtering (CPCF) is introduced as an algorithm of the VFM model, and this curve provides good nonlinear...
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