'Acoustic modeling' diaporamas de présentation

Speech Recognition

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LSA 352: Speech Recognition and Synthesis

LSA 352: Speech Recognition and Synthesis. Dan Jurafsky. Lecture 5: Intro to ASR+HMMs: Forward, Viterbi, Baum-Welch. IP Notice:. Outline for Today. Speech Recognition Architectural Overview Hidden Markov Models in general Forward Viterbi Decoding Baum-Wlech Applying HMMs to speech

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LSA 352: Speech Recognition and Synthesis

LSA 352: Speech Recognition and Synthesis. Dan Jurafsky. Lecture 5: Intro to ASR+HMMs: Forward, Viterbi, Baum-Welch. IP Notice:. Outline for Today. Speech Recognition Architectural Overview Hidden Markov Models in general Forward Viterbi Decoding Baum-Wlech Applying HMMs to speech

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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

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SPEECH RECOGNITION:

SPEECH RECOGNITION:. An Overview of Statistical Modeling of Acoustics. Joseph Picone, PhD Intelligent Electronic Systems Human and Systems Engineering Department of Electrical and Computer Engineering. Abstract and Biography.

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Seminar Speech Recognition Project Support E.M. Bakker LIACS Media Lab (LML) Leiden University

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On Organic Interfaces

On Organic Interfaces. Victor Zue (zue@csail.mit.edu) MIT Computer Science and Artificial Intelligence Laboratory. Graduate Students. Anderson, M. Aull, A. Brown, R. Chan, W. Chang, J. Chang, S. Chen, C. Cyphers, S. Daly, N. Doiron, R. Flammia, G. Glass, J. Goddeau, D.

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Joseph Picone, PhD Professor, Electrical Engineering Mississippi State University

Can Advances in Speech Recognition make Spoken Language as Convenient and as Accessible as Online Text?. Joseph Picone, PhD Professor, Electrical Engineering Mississippi State University. Patti Price, PhD VP Business Development BravoBrava. Outline.

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Dynamic data selection in search (1/4)

A Dynamic In-Search Data Selection Method With Its Application to Acoustic Modeling and Utterance Verification (about training). Dynamic data selection in search (1/4).

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Speech Recognition Introduction I

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Speech Recognition Introduction I

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Objectives: Acoustic Modeling Language Modeling Feature Extraction Search Pronunciation Modeling

LECTURE 29: EXAMPLES OF PATTERN RECOGNITION IN SPEECH RECOGNITION. Objectives: Acoustic Modeling Language Modeling Feature Extraction Search Pronunciation Modeling

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Physically Based Sound Modeling

Physically Based Sound Modeling. Class #13 (Feb 24) Doug James, CMU. Overview Class #13 (Feb 24). Physically based sound modeling Important aspects of PB sound: generation (impact, vibration, ...) emission propagation listening. Properties of Sound Waves. Speed:

377 views • 22 slides

Physically Based Sound Modeling

Physically Based Sound Modeling. Class #13 (Feb 24) Doug James, CMU. Overview Class #13 (Feb 24). Physically based sound modeling Important aspects of PB sound: generation (impact, vibration, ...) emission propagation listening. Properties of Sound Waves. Speed:

366 views • 22 slides

Automatic Equalization for Live Venue Sound Systems

Automatic Equalization for Live Venue Sound Systems. Damien Dooley, Final Year ECE. Initial Presentation, Tuesday 2 nd October 2007. Contents. Background Project Description Initial Work (Room Acoustic Modeling). Background.

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Acoustic Modeling

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LECTURE 28: STATE OF THE ART. Objectives: Language Modeling in ASR Discriminative Feature Mapping Example System Course Evaluations Resources: MB: Unsupervised LM Adaptation RS: Statistical Language Modeling DP: Discriminatively Trained Features AS: Discriminative Adaptation IBM: GALE Mandarin.

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