'Hmm' diaporamas de présentation

S2S Nijmegen Workshop February 10-14, 2008

Combining Abstract and Exemplar Models in ASR Dirk Van Compernolle Kris Demuynck, Mathias De Wachter. S2S Nijmegen Workshop February 10-14, 2008. Overview. PART I: Example based models in ASR: Motivation Proof-of-Concept Baseline Results Required Extensions

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An Approach to ECG Delineation using Wavelet Analysis and Hidden Markov Models

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gPU -ACCELERATED hmm FOR Speech Recognition

HUCAA 2014. gPU -ACCELERATED hmm FOR Speech Recognition. Leiming Yu, Yash Ukidave and David Kaeli ECE, Northeastern University. Outline. Background & Motivation HMM GPGPU Results Future Work. Background. Translate Speech to Text Speaker Dependent Speaker Independent

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Factor Analysis of Acoustic Features for Streamed Hidden Markov Modeling

Factor Analysis of Acoustic Features for Streamed Hidden Markov Modeling. Chuan-Wei Ting Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan. Outline. Introduction Cepstral Factor Analysis FA Streamed Hidden Markov Model Experiments

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Hidden Markov Models in Bioinformatics

Hidden Markov Models in Bioinformatics. Example Domains: Gene Finding & Protein Family Modeling Colin Cherry colinc@cs. My Credentials. By day, I do NLP work with Dekang Lin Worked with HMMs in the 1 st term of my 1 st year here

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Hidden Markov Models in Bioinformatics

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Comparative Gene Finding

Comparative Gene Finding. B. Majoros. What is Comparative Gene Finding?. Problem: Predict genes in a target genome S based on the contents of S and also based on the contents of one or more informant genomes I (1) ... I ( n ) :.

449 views • 33 slides

Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector Time Series

Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector Time Series. Sergey Kirshner, UC Irvine Padhraic Smyth, UC Irvine Andrew Robertson, IRI. July 10, 2004. Overview. Data and its modeling aspects Model description General Approach Hidden Markov models

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Audio-visual speaker verification using continuous fused HMMs

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Tagging with Hidden Markov Models

Tagging with Hidden Markov Models. CMPT 882 Final Project Chris Demwell Simon Fraser University. The Tagging Task. Identification of the part of speech of each word of a corpus Supervised: Training corpus provided consisting of correctly tagged text Unsupervised: Uses only plain text.

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Multimodal Group Action Clustering in Meetings

Multimodal Group Action Clustering in Meetings. Dong Zhang, Daniel Gatica-Perez, Samy Bengio, Iain McCowan , Guillaume Lathoud IDIAP Research Institute Switzerland. Outline. Meetings: Sequences of Actions. Why Clustering? Layered HMM Framework. Experiments. Conclusion and Future Work.

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Part-of-Speech Tagging for Bengali with Hidden Markov Model

Part-of-Speech Tagging for Bengali with Hidden Markov Model. Sandipan Dandapat, Sudeshna Sarkar Department of Computer Science & Engineering Indian Institute of Technology Kharagpur. Machine Learning to Resolve POS Tagging. HMM Supervised (DeRose,88; Mcteer,91; Brants,2000; etc.)

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Advanced Algorithms and Models for Computational Biology -- a machine learning approach

Advanced Algorithms and Models for Computational Biology -- a machine learning approach. Computational Genomics II: HMM variants and Comparative Gene Finding Eric Xing Lecture 5, February 1, 2005. Reading: Chap 3, 5 DEKM book Chap 9, DTW book . Higher-order HMMs. The Genetic Code

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Speech Signal Processing I

Speech Signal Processing I. By Edmilson Morais And Prof. Greg. Dogil Stuttgart, October 18, 2001. Goals of the Course. Our part Basic theoretical concepts about Speech Signal Processing - SDSP Waveform generation for TTS systems - TTS

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Hidden Markov Model: Overview and Applications in MIR

Hidden Markov Model: Overview and Applications in MIR. MUMT 611, March 2005 Paul Kolesnik. Contents. Introduction to HMM Overview of Publications Conclusion. Introduction. Definition A structure that is used to statistically characterize the behavior of sequences of event observations

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Mode-Directed Tabling for Dynamic Programming, Machine Learning, and Constraint Solving

Mode-Directed Tabling for Dynamic Programming, Machine Learning, and Constraint Solving. Neng-Fa Zhou (City Univ. of New York) Yoshitaka Kameya and Taisuke Sato (Tokyo Inst. of Technology). What is Tabling?.

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Applications of Hidden Markov Models

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COLACL 2006 Segment-based Hidden Markov Models for Information Extraction

COLACL 2006 Segment-based Hidden Markov Models for Information Extraction. Zhenmei Gu David R. Cheriton School of Computer Science University of Waterloo Nick Cercone Faculty of Computer Science Dalhousie University. JSYU, 2006.09.14. Outlines. Introduction Problem description

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