'Gaussian mixture models' diaporamas de présentation

Unsupervised Learning II: Soft Clustering with Gaussian Mixture Models

968 views • 96 slides

Speech and Language Processing

Speech and Language Processing. Chapter 9 of SLP Automatic Speech Recognition. Outline for ASR. ASR Architecture The Noisy Channel Model Five easy pieces of an ASR system Feature Extraction Acoustic Model Language Model Lexicon/Pronunciation Model (Introduction to HMMs again) Decoder

732 views • 59 slides

Flat clustering approaches for high-throughput omic datasets

Flat clustering approaches for high-throughput omic datasets. Sushmita Roy BMI/CS 576 www.biostat.wisc.edu/bmi576 sroy@biostat.wisc.edu Nov 7 th , 2013. Key concepts to take away. What are high-throughput methods and datasets?

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Machine Learning and Data Mining Clustering

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A Left-to- Right HDP - HMM with HDPM Emissions

A Left-to- Right HDP - HMM with HDPM Emissions. Amir Harati , Joseph Picone and Marc Sobel Institute for Signal and Information Processing Temple University Philadelphia, Pennsylvania, USA. Abstract.

345 views • 23 slides

Modeling of Mel Frequency Features for Non Stationary Noise

Modeling of Mel Frequency Features for Non Stationary Noise. I.Andrianakis P.R.White. Signal Processing and Control Group Institute of Sound and Vibration Research University of Southampton. Outline. Introduction. Mel Frequency Log Spectrum and Cepstrum.

357 views • 22 slides

Nonlinear Mixture Autoregressive Hidden Markov Models for Speech Recognition

Nonlinear Mixture Autoregressive Hidden Markov Models for Speech Recognition. S. Srinivasan , T. Ma, D. May, G. Lazarou and J. Picone Department of Electrical and Computer Engineering Mississippi State University

770 views • 19 slides

Statistical techniques for video analysis and searching chapter 9

Statistical techniques for video analysis and searching chapter 9. 2010-11806 Anton Korotygin. Contents. Introduction Model Vectors Video Search Fusion Experiments Conclusion. Before Starting.

320 views • 19 slides

Shape Analysis for Microscopy

Shape Analysis for Microscopy. Kangyu Pan in collaboration with: Jens Hillebrand, Mani Ramaswami Institute for Neuroscience Trinity College Dublin & Michael J. Higgins Intelligent Polymer Research Institute University of Wollongong, Australia. Jens Hillebrand, Mani Ramaswami

289 views • 18 slides

EE-148 Expectation Maximization

EE-148 Expectation Maximization. Markus Weber 5/11/99. Overview. Expectation Maximization is a technique used to estimate probability densities under missing (unobserved) data. Density Estimation Observed vs. Missing Data EM. Probability Density Estimation Why is it important?.

441 views • 14 slides

Automatic Speaker Recognition in Military Environment

Automatic Speaker Recognition in Military Environment. Corinna Harwardt 28.10.2009. Overview. Basics of automatic speaker recognition The VerA system GMM-UBM based system Results on military relevant audio data High-Level Features for improved speaker recognition

419 views • 14 slides

Automatic Speaker Recognition in Military Environment

Automatic Speaker Recognition in Military Environment. Corinna Harwardt 28.10.2009. Overview. Basics of automatic speaker recognition The VerA system GMM-UBM based system Results on military relevant audio data High-Level Features for improved speaker recognition

311 views • 14 slides

Voice Recognition

Voice Recognition. All Talk No Walk. Different types of Recogniton. Speech Recognition What is being said Speaker Recognition Who is speaking Speaker D iarisation Voice Recognition (Combination) More accurate. Verification vs. Identification. Verification, Comparing two samples 1:1

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144 views • 1 slides