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An Exemplar-based Approach to Automatic Burst Detection in Voiceless Stops

An Exemplar-based Approach to Automatic Burst Detection in Voiceless Stops

An Exemplar-based Approach to Automatic Burst Detection in Voiceless Stops. YAO YAO UC BERKELEY YAOYAO@BERKELEY.EDU http://linguistics.berkeley.edu/~yaoyao JULY 25, 2008. Overview. Background Data Methodology Algorithm Tuning the model Testing Results General Discussion.

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662 views • 32 slides
An Exemplar-based Approach to Automatic Burst Detection in Voiceless Stops

An Exemplar-based Approach to Automatic Burst Detection in Voiceless Stops

An Exemplar-based Approach to Automatic Burst Detection in Voiceless Stops. YAO YAO UC BERKELEY YAOYAO@BERKELEY.EDU http://linguistics.berkeley.edu/~yaoyao JULY 25, 2008. Overview. Background Data Methodology Algorithm Tuning the model Testing Results General Discussion.

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400 views • 32 slides
Application of Signal and Noise Theory to Digital VLSI Testing

Application of Signal and Noise Theory to Digital VLSI Testing

Application of Signal and Noise Theory to Digital VLSI Testing. Vishwani D. Agrawal Auburn University, Dept. of Elec. & Comp. Engg. Auburn, AL 36849, U.S.A. Nitin Yogi NVIDIA Corporation, Santa Clara, CA 95050. 20 th April 2010 28 th IEEE VLSI Test Symposium Santa Cruz, CA.

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345 views • 21 slides
TIME-FREQUENCY PRINCIPAL COMPONENTS OF SPEECH: APPLICATION TO SPEAKER IDENTIFICATION

TIME-FREQUENCY PRINCIPAL COMPONENTS OF SPEECH: APPLICATION TO SPEAKER IDENTIFICATION

Principle of the Time-Frequency Principal Components (TFPC) Analysis:. Results:. ? Expanded vectors: ? Contextual Covariance Matrix: covariance matrix of the expanded vectors. ? Time-Frequency principal Components: principal components of the contextual covariance matrix.

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123 views • 1 slides

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