Chuan-Wei Ting

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We present a novel subspace modeling and selection approach for noisy speech recognition. In subspace modeling, we develop a factor analysis (FA) representation of noisy speech, which is a generalization of a signal subspace (SS) representation. Using FA, noisy speech is represented by the extracted common factors, factor loading matrix, and specific(More)
Gaussian mixture model (GMM) techniques are popular for speaker identification. Theoretically, each Gaussian function should have a full covariance matrix. However, the diagonal covariance matrix is usually used because the inverse of diagonal covariance matrix can be easily calculated via expectation maximization (EM) algorithm. This paper proposes a new(More)
This paper presents a novel streamed hidden Markov model (HMM) framework for speech recognition. The factor analysis (FA) principle is adopted to explore the common factors from acoustic features. The streaming regularities in building HMMs are governed by the correlation between cepstral features, which is inherent in common factors. Those features(More)
Multiscale entropy (MSE) is a measurement for quantifying the randomness of a sequence of data. Recently, it has been proven to be the most effective way to analyze the complexity of physiological signals in biomedicine and other fields. The implementation of MSE is computationally expensive because it considers multiple complexities of several data(More)