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Based on the analysis and comparisons of complexity between stochastic segment model (SSM) and hidden Markov model (HMM) in this paper, we presented a fast and robust SSM, which yields a 94.75% speaker-independent performance on Mandarin digit string recognition. This result is better than HMM based system at the same level of computational complexity and just only a little slower than HMM in the...
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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