'Language model' diaporamas de présentation

Spoken Dialogue Systems

Spoken Dialogue Systems. Bob Carpenter and Jennifer Chu-Carroll June 20, 1999. Tutorial Overview: Data Flow. Part I. Part II. Semantic Interpretation. Signal Processing. Speech Recognition. Discourse Interpretation. Dialogue Management. Speech Synthesis. Response Generation.

1.6k views • 124 slides

Lexical networks, lexical centrality, and text mining

Computational Linguistics And Information Retrieval @ Michigan. Lexical networks, lexical centrality, and text mining. Dragomir R. Radev Associate Professor University of Michigan CSE, SI, LING radev@umich.edu. The CLAIR gang. This talk is based on joint work with Vahed Qazvinian

1.27k views • 105 slides

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767 views • 62 slides

Data-Intensive Distributed Computing

Data-Intensive Distributed Computing. CS 431/631 451/651 (Fall 2019). Part 3: Analyzing Text (1/2). September 26, 2019. Ali Abedi. These slides are available at https://www.student.cs.uwaterloo.ca/~cs451.

755 views • 60 slides

Learning from Natural Instructions

Learning from Natural Instructions. Dan Roth Department of Computer Science University of Illinois at Urbana-Champaign. With thanks to: Collaborators: Ming-Wei Chang, James Clarke, Michael Connor, Dan Goldwasser , Lev Ratinov, Vivek Srikumar, Many others

648 views • 57 slides

Large Vocabulary Continuous Speech Recognition (LVCSR)

Large Vocabulary Continuous Speech Recognition (LVCSR). Automatic Speech Recognition Spring 2016. Large Vocabulary Continuous Speech Recognition. Sub-word Speech Units. HMM-Based Sub-word Speech Units. Training of Sub-word Units. Training of Sub-word Units. Training Procedure.

974 views • 53 slides

Minimum Classification Error (MCE) Approach in Pattern Recognition

Minimum Classification Error (MCE) Approach in Pattern Recognition. Wu Chou, Avaya Labs Research, Avaya Inc., USA. Outline (1/2). Introduction Optimal Classifier from Bayes Desicion Theory Discriminant Function Approach to Classifier Design Speech Recogniation and Hidden Markov Modeling

783 views • 53 slides

CS 4705

628 views • 48 slides

Sound and Speech Recognition

Sound and Speech Recognition. What is Sound ?. Acoustics is the study of sound. Physical - sound as a disturbance in the air Psychophysical - sound as perceived by the ear Sound as stimulus (physical event) & sound as a sensation. Pressures changes (in band from 20 Hz to 20 kHz)

784 views • 45 slides

Language Models for Information Retrieval

Language Models for Information Retrieval. Berlin Chen Department of Computer Science & Information Engineering National Taiwan Normal University. References: 1. W. B. Croft and J. Lafferty (Editors). Language Modeling for Information Retrieval. July 2003

1.21k views • 42 slides

MODELLING LATVIAN LANGUAGE FOR AUTOMATIC SPEECH RECOGNITION

MODELLING LATVIAN LANGUAGE FOR AUTOMATIC SPEECH RECOGNITION. Askars Salimbajevs. Supervisor: Prof. Inguna Skadi?a University of Latvia. Introduction Acoustic modelling Automatic data acquisition Pronunciation model Language modelling Text preprocessing Word and sub-word N-gram models

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555 views • 41 slides

SEDU 718

521 views • 26 slides

WHOLE LANGUAGE MODEL FOR PROVIDING SPEECH THERAPY IN VCFS PATIENTS

WHOLE LANGUAGE MODEL FOR PROVIDING SPEECH THERAPY IN VCFS PATIENTS. CLINICAL RESEARCH IN SPEECH PATHOLOGY. EARLY SURGERY PREVENTS COMPENSATORY ARTICULATION THE OLDER THE PATIENT IS WHEN PALATE IS REPAIRED, THE GREATER THE RISK FOR DEVELOPING COMPENSATORY ARTICULATION.

427 views • 25 slides

Semantic Smoothing of Document Models for Agglomerative Clustering

Semantic Smoothing of Document Models for Agglomerative Clustering. Xiaohua Zhou , Xiaodan Zhang, Tony Hu College of Information Science & Technology Drexel University, USA. Agglomerative Clustering. Algorithm Overview

620 views • 23 slides

SCILL: Spoken Conversational Interaction for Language Learning

SCILL: Spoken Conversational Interaction for Language Learning . Stephanie Seneff (seneff@csail.mit.edu) Jim Glass (jrg@csail.mit.edu) Spoken Language Systems Group MIT Computer Science and Artificial Intelligence Lab Steve Young (sjy@eng.cam.ac.uk) Speech Group

383 views • 22 slides

HUMAN LANGUAGE TECHNOLOGY: From Bits to Blogs

HUMAN LANGUAGE TECHNOLOGY: From Bits to Blogs. Joseph Picone, PhD Professor and Chair Department of Electrical and Computer Engineering Temple University. URL:. Abstract. What makes machine understanding of human language so difficult?

319 views • 20 slides

IBM Statistical Machine Translation for Spoken Languages

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Overview of Statistical Language Models

Overview of Statistical Language Models. ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign. Outline. What is a statistical language model (SLM)? Brief history of SLM Types of SLM Applications of SLM. What is a Statistical Language Model (LM)?.

682 views • 18 slides

The HTK Book (for HTK Version 3.2.1)

The HTK Book (for HTK Version 3.2.1). Young et al., 2002. Chapter 1 The Fundamentals of HTK. HTK is a toolkit for building hidden Markov models (HMMs). Primarily used to build ASRs, but also other HMM systems: speaker and image recognition, automatic text summarization etc.

525 views • 13 slides