'Discriminative training' presentation slideshows

Human Speech Perception

Human Speech Perception. Lecture 13 Spoken Language Processing Prof. Andrew Rosenberg. Linguistics View of Speech Recognition. Speech is a sequence of articulatory gestures Many parallel levels of description Phonetic, Phonologic Prosodic Lexical Syntactic, Semantic, Pragmatic

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From Sequence to Expression: A Probabilistic Framework

From Sequence to Expression: A Probabilistic Framework. Joint work with:. Eran Segal (Stanford). Nir Friedman (Hebrew U.) Daphne Koller (Stanford). Yoseph Barash (Hebrew U.) Itamar Simon (Whitehead Inst.). G1. S. M. G2. Understanding Cellular Processes.

425 views • 33 slides

ICASSP 2009: Acoustic Model Survey

ICASSP 2009: Acoustic Model Survey. Yueng-Tien,Lo. Discriminative Training of Hierarchical Acoustic Models For Large Vocabulary Continuous Speech Recognition. Hung-An Chang and James R. Glass MIT Computer Science and Artificial Intelligence Laboratory Cambridge, Massachusetts, 02139, USA

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Discriminative Training in Speech Processing

Discriminative Training in Speech Processing. Filipp Korkmazsky LORIA. Content. Bayes Decision Theory and DiscriminativeTraining Minimum Classification Error(MCE) Training Generalized Probabilistic Descent(GPD) algorithm MCE Training versus Maximum Mutual

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Discriminative Models for Spoken Language Understanding

Discriminative Models for Spoken Language Understanding. Ye-Yi Wang, Alex Acero Microsoft Research, Redmond, Washington USA ICSLP 2006. Outline. Abstract Introduction Conditional models for SLU Discriminative training Conditional random fields (CRFs) Perceptron

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Min-Hsuan Lai Department of Computer Science & Information Engineering National Taiwan Normal University

DISCRIMINATIVE TRAINING OF LANGUAGE MODELS FOR SPEECH RECOGNITION Hong-Kwang Jeff Kuo, Eric Fosler-Lussier, Hui Jiang, Chin-Hui Lee ICASSP 2002. Min-Hsuan Lai Department of Computer Science & Information Engineering National Taiwan Normal University. Outline. Introduction

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NUDT Machine Translation System for IWSLT2007

NUDT Machine Translation System for IWSLT2007. Presenter: Boxing Chen Authors: Wen-Han Chao & Zhou-Jun Li National University of Defense Technology, China. Outline. System Description Word Alignment Phrase Pair Extracting Example-Based Decoder Experiments Future Works.

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On-the-fly Specific Person Retrieval

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Discriminative Training of Chow-Liu tree Multinet Classifiers

Discriminative Training of Chow-Liu tree Multinet Classifiers. Huang, Kaizhu Dept. of Computer Science and Engineering, CUHK. Outline. Background Classifiers Discriminative classifiers Generative classifiers Bayesian Multinet Classifiers Motivation

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A Hybrid Model of HMM and RBFN Model of Speech Recognition

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Acoustic models in Kaldi

Acoustic models in Kaldi . Acoustic models in Kaldi . Support for standard ML-trained models Linear transforms like LDA, HLDA, MLLT/STC Speaker adaptation with fMLLR, MLLR Support for tied-mixture systems initially discussed Support for SGMMs

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LECTURE 25: DISCRIMINATIVE TRAINING

LECTURE 25: DISCRIMINATIVE TRAINING. Objectives: Bayes Rule Mutual Information Conditional Likelihood Mutual Information Estimation (CMLE) Maximum MI Estimation (MMIE) Minimum Classification Error (MCE)

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LECTURE 31: DISCRIMINATIVE TRAINING

LECTURE 31: DISCRIMINATIVE TRAINING. Objectives: Bayes Rule Mutual Information Conditional Likelihood Mutual Information Estimation (CMLE) Maximum MI Estimation (MMIE) Minimum Classification Error (MCE)

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LECTURE 25: DISCRIMINATIVE TRAINING

LECTURE 25: DISCRIMINATIVE TRAINING. Objectives: Bayes Rule Mutual Information Conditional Likelihood Mutual Information Estimation (CMLE) Maximum MI Estimation (MMIE) Minimum Classification Error (MCE)

133 views • 12 slides

LECTURE 33: DISCRIMINATIVE TRAINING

LECTURE 33: DISCRIMINATIVE TRAINING. Objectives: Bayes Rule Mutual Information Conditional Likelihood Mutual Information Estimation (CMLE) Maximum MI Estimation (MMIE) Minimum Classification Error (MCE)

167 views • 6 slides