'Hmm parameters' diaporamas de présentation

Fast Temporal State-Splitting for HMM Model Selection and Learning

Fast Temporal State-Splitting for HMM Model Selection and Learning. Sajid Siddiqi Geoffrey Gordon Andrew Moore. x. t. x. t. How many kinds of observations (x) ?. x. t. How many kinds of observations (x) ? 3. x. t. How many kinds of observations (x) ? 3

849 views • 74 slides

Problem in biology

CZ5226: Advanced Bioinformatics Lecture 6: HHM Method for generating motifs Prof. Chen Yu Zong Tel: 6874-6877 Email: csccyz@nus.edu.sg http://xin.cz3.nus.edu.sg Room 07-24, level 7, SOC1, National University of Singapore. Problem in biology. Data and patterns are often not clear cut

742 views • 64 slides

Discriminative Training and Machine Learning Approaches

Discriminative Training and Machine Learning Approaches. Chih-Pin Liao. Machine Learning Lab, Dept. of CSIE, NCKU. Discriminative Training. Our Concerns. Feature extraction and HMM modeling should be jointly performed. Common objective function should be considered.

408 views • 40 slides

CS 552/652 Speech Recognition with Hidden Markov Models Winter 2011

CS 552/652 Speech Recognition with Hidden Markov Models Winter 2011 Oregon Health & Science University Center for Spoken Language Understanding John-Paul Hosom Lecture 18 March 9 Acoustic-Model Strategies for Improved Performance. Next Topics: Improving Performance of an HMM.

587 views • 37 slides

A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition

667 views • 33 slides

Speech Recognition

361 views • 33 slides

Hidden Markov Models in Bioinformatics

888 views • 32 slides

Soft Margin Estimation for Speech Recognition

Soft Margin Estimation for Speech Recognition. Main Reference: Jinyu Li , " SOFT MARGIN ESTIMATION FOR AUTOMATIC SPEECH RECOGNITION ," PhD thesis, Georgia Institute of Technology School of Electrical and Computer Engineering , 2008. Yueng-Tien , Lo Speech Lab

397 views • 27 slides

S tochastic V ector M apping

S tochastic V ector M apping. ShihHsiang 2006. Reference. [ICSLP 2004] A S tudy of Minimum Classification Error Training for Segmental Switching Linear Gaussian Hidden Markov Models

252 views • 25 slides

Mr. Yik-Cheung Tam Dr. Brian Mak

An Alternative Approach of Finding Competing Hypotheses for Better Minimum Classification Error Training. Mr. Yik-Cheung Tam Dr. Brian Mak. Outline. Motivation Overview of MCE training Problem using N-best hypotheses Alternative:1-nearest hypothesis What? Why? How? Evaluation

311 views • 21 slides

Ab Initio Profile HMM Generation

Ab Initio Profile HMM Generation. Sam Gross. STOLEN FROM BATZOGLOU LECTURE. D m-1. D m. D 1. D 2. BEGIN. END. I 0. I 1. I m-1. I m. M 1. M 2. M m. Profile HMMs. Each M state has a position-specific pre-computed substitution table

204 views • 13 slides