'Maximum likelihood linear regression' presentation slideshows

CS 224S / LINGUIST 285 Spoken Language Processing

CS 224S / LINGUIST 285 Spoken Language Processing. Dan Jurafsky Stanford University Spring 2014 Lecture 6: Feature Extraction. Outline. Feature extraction How to compute MFCCs Dealing with variation Adaptation MLLR MAP Lombard speech Foreign accent Pronunciation variation.

983 views • 81 slides

Automatic Dialect/Accent Recognition

Automatic Dialect/Accent Recognition. Fadi Biadsy April 12 th , 2010. Outline. Problem Motivation Corpora Framework for Language Recognition Experiments in Dialect Recognition Phonotactic Modeling Prosodic Modeling Acoustic Modeling Discriminative Phonotactics .

911 views • 50 slides

Lecture 24: Model Adaptation and Semi-Supervised Learning

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Domain Adaptation with Structural Correspondence Learning

Domain Adaptation with Structural Correspondence Learning. John Blitzer. Joint work with. Shai Ben-David, Koby Crammer, Mark Dredze, Ryan McDonald, Fernando Pereira. Statistical models, multiple domains. politics blogs. tech blogs. Different Domains of Text.

670 views • 35 slides

Lecture 20: Model Adaptation

Lecture 20: Model Adaptation. Machine Learning April 15, 2010. Today. Adaptation of Gaussian Mixture Models Maximum A Posteriori (MAP) Maximum Likelihood Linear Regression (MLLR) Application: Speaker Recognition UBM-MAP + SVM. The Problem.

456 views • 35 slides

ASR and scalability

344 views • 18 slides

Regularized Adaptation for Discriminative Classifiers

351 views • 16 slides

LECTURE 16: EM AND SIMPLE REGRESSION. Objectives: Feature and Model Space Adaptation General Adaptation Framework Expectation Maximization Simple Linear Regression

227 views • 12 slides