Lexical Stress in Speech Recognition
|
|
|
- Jesse Gallagher
- 7 years ago
- Views:
Transcription
1 Lexical Stress in Speech Recognition Master s thesis presentation Rogier van Dalen 13th May Delft University of Technology
2 Topics Objective What is lexical stress? Properties of lexical stress Model System Results 13th May
3 Objective Can lexical stress be used in a speech recogniser to make it perform better? 13th May
4 Objective Can lexical stress be used in a speech recogniser to make it perform better? Find properties of lexical stress 13th May
5 Objective Can lexical stress be used in a speech recogniser to make it perform better? Find properties of lexical stress Model speech recogniser 13th May
6 Objective Can lexical stress be used in a speech recogniser to make it perform better? Find properties of lexical stress Model speech recogniser Implement speech recogniser 13th May
7 Objective Can lexical stress be used in a speech recogniser to make it perform better? Find properties of lexical stress Model speech recogniser Implement speech recogniser Test speech recogniser 13th May
8 Garden-variety speech recognition Input modelled as a concatenation of phonemes 13th May
9 What is lexical stress? 13th May
10 What is lexical stress? Hoor ik daar een kanon? 13th May
11 What is lexical stress? Hoor ik daar een kanon? kanón gun or kánon song? 13th May
12 Use of lexical stress Minimal pairs 13th May
13 Use of lexical stress Minimal pairs (a) subject (to) subject 13th May
14 Use of lexical stress Minimal pairs (a) subject (to) subject Du. aanbod offer aan bod first in line 13th May
15 Use of lexical stress Minimal pairs (a) subject (to) subject Du. aanbod offer aan bod first in line Du. voorkomen prevent voorkomen happen 13th May
16 Use of lexical stress Minimal pairs (a) subject (to) subject Du. aanbod offer aan bod first in line Du. voorkomen prevent voorkomen happen Portuguese falara I had spoken falará he will speak 13th May
17 Use of lexical stress 13th May
18 Use of lexical stress Word recognition 13th May
19 Use of lexical stress Word recognition Du. october octopus 13th May
20 Use of lexical stress Word recognition Du. october octopus tigress digress 13th May
21 Use of lexical stress 13th May
22 Use of lexical stress Segmentation 13th May
23 Use of lexical stress Segmentation conduct ascends uphill 13th May
24 Use of lexical stress Segmentation conduct ascends uphill a doctor sends a pill? 13th May
25 Properties of lexical stress 13th May
26 Properties of lexical stress Stress works on the syllable level 13th May
27 A lexicon a are the garden ordinary table variety /ei/ /A:/ /Di:/ /ga:d@n/ /O:dInEri/ /teib@l/ /v@rai@ti/ 13th May
28 A lexicon a /ei/ [@] are /A:/ the /Di:/ garden /ga:d@n/ ordinary /O:dInEri/ table /teib@l/ variety /v@rai@ti/ 13th May
29 A lexicon a /ei/ [@] are /A:/ [@] the /Di:/ garden /ga:d@n/ ordinary /O:dInEri/ table /teib@l/ variety /v@rai@ti/ 13th May
30 A lexicon a /ei/ [@] are /A:/ [@] the /Di:/ [D@] garden /ga:d@n/ ordinary /O:dInEri/ table /teib@l/ variety /v@rai@ti/ 13th May
31 A lexicon with stress marks a /ei/ [@] are /A:/ [@] the /Di:/ [D@] garden /"ga:d@n/ ordinary /O:dInEri/ table /teib@l/ variety /v@rai@ti/ 13th May
32 A lexicon with stress marks a /ei/ [@] are /A:/ [@] the /Di:/ [D@] garden /"ga:d@n/ ordinary /O:dInEri/ ["ga:dn] " table /teib@l/ variety /v@rai@ti/ 13th May
33 A lexicon with stress marks a /ei/ [@] are /A:/ [@] the /Di:/ [D@] garden /"ga:d@n/ ordinary /"O:dInEri/ ["ga:dn] " table /teib@l/ variety /v@rai@ti/ 13th May
34 A lexicon with stress marks a /ei/ [@] are /A:/ [@] the /Di:/ [D@] garden /"ga:d@n/ ["ga:dn] " ordinary /"O:dInEri/ ["O:dn ri] " table /teib@l/ variety /v@rai@ti/ 13th May
35 A lexicon with stress marks a /ei/ [@] are /A:/ [@] the /Di:/ [D@] garden /"ga:d@n/ ["ga:dn] " ordinary /"O:dInEri/ ["O:dn ri] " table /"teib@l/ variety /v@rai@ti/ 13th May
36 A lexicon with stress marks a /ei/ [@] are /A:/ [@] the /Di:/ [D@] garden /"ga:d@n/ ["ga:dn] " ordinary /"O:dInEri/ ["O:dn ri] " table /"teib@l/ ["t h eibl ] " variety /v@rai@ti/ 13th May
37 A lexicon with stress marks a /ei/ [@] are /A:/ [@] the /Di:/ [D@] garden /"ga:d@n/ ["ga:dn] " ordinary /"O:dInEri/ ["O:dn ri] " table /"teib@l/ ["t h eibl ] " variety /v@"rai@ti/ 13th May
38 A lexicon with stress marks a /ei/ [@] are /A:/ [@] the /Di:/ [D@] garden /"ga:d@n/ ["ga:dn] " ordinary /"O:dInEri/ ["O:dn ri] " table /"teib@l/ ["t h eibl ] " variety /v@"rai@ti/ [v"rai@ti] " 13th May
39 Properties of lexical stress Stress works on the syllable level 13th May
40 Properties of lexical stress Stress works on the syllable level Unstressed syllables are reduced 13th May
41 /ka:"non/ gun or /"ka:non/ song? Pitch (Hz) Pitch (Hz) Time (s) Time (s) k a: n O n k a: n O n 13th May
42 /ka:"non/ gun or /"ka:non/ song? Pitch (Hz) Pitch (Hz) Time (s) Time (s) k a: n O n k a: n O n kanón /ka:"non/ gun kánon /"ka:non/ song 13th May
43 /"ka:non?/ song? or /ka:"non?/ gun?? Pitch (Hz) Pitch (Hz) Time (s) Time (s) k a: n O n k a: n O n 13th May
44 /"ka:non?/ song? or /ka:"non?/ gun?? Pitch (Hz) Pitch (Hz) Time (s) Time (s) k a: n O n k a: n O n kanón /ka:"non/ gun kánon /"ka:non/ song 13th May
45 /"ka:non/ song or /ka:"non/ gun? Time (s) Time (s) k a: n O n k a: n O n 13th May
46 /"ka:non/ song or /ka:"non/ gun? Time (s) Time (s) k a: n O n k a: n O n kanón /ka:"non/ gun kánon /"ka:non/ song 13th May
47 Properties of lexical stress Stress works on the syllable level Unstressed syllables are reduced 13th May
48 Properties of lexical stress Stress works on the syllable level Unstressed syllables are reduced Stressed syllables have longer durations 13th May
49 Properties of lexical stress Stress works on the syllable level Unstressed syllables are reduced Stressed syllables have longer durations Stressed syllables are louder 13th May
50 /ka:"non/ gun or /"ka:non/ song? Frequency (Hz) Frequency (Hz) Time (s) Time (s) k a: n O n k a: n O n 13th May
51 /ka:"non/ gun or /"ka:non/ song? Frequency (Hz) Frequency (Hz) Time (s) Time (s) k a: n O n k a: n O n kanón /ka:"non/ gun kánon /"ka:non/ song 13th May
52 /"ka:non?/ song? or /ka:"non?/ gun?? Frequency (Hz) Frequency (Hz) Time (s) Time (s) k a: n O n k a: n O n 13th May
53 /"ka:non?/ song? or /ka:"non?/ gun?? Frequency (Hz) Frequency (Hz) Time (s) Time (s) k a: n O n k a: n O n kanón /ka:"non/ gun kánon /"ka:non/ song 13th May
54 Properties of lexical stress Stress works on the syllable level Unstressed syllables are reduced Stressed syllables have longer durations Stressed syllables are louder 13th May
55 Properties of lexical stress Stress works on the syllable level Unstressed syllables are reduced Stressed syllables have longer durations Stressed syllables are louder Stressed syllables have more high frequencies 13th May
56 Distinguishing phonemes /y:/ /u:/ /O/ /a:/ 13th May
57 Distinguishing phonemes /"y:/ /"u:/ /y:/ /u:/ /a:/ /O/ /"O/ /"a:/ 13th May
58 Integration in a speech recogniser /A a: p t O v V/ 13th May
59 Integration in a speech recogniser /A a: p t O v V/ Stressed and unstressed versions of phonemes /A "A a: "a: p "p t "t O "O v "v V "V/ 13th May
60 Integration in the lexicon are a the garden ordinary g A: d n O: d n r i t ei b l v r t i 13th May
61 Integration in the lexicon are a the garden ordinary "g "A: d n "O: d n r i "t "ei b l v "r t i 13th May
62 Integration in feature vectors 13th May
63 Integration in feature vectors MFCCs Spectral tilt features 13th May
64 Modelling duration a 22 a 33 a 44 a 12 a 23 a a b 2 b 3 b 4 13th May
65 Model baseline Feature extraction Viterbi: phoneme level { ei l { } i { } Viterbi: m n word level alien ei i l m n a /@/ alien /eili@n/ lion /lai@n/ hmms Lexicon 13th May
66 Model stress-enabled Stress feature extraction Feature extraction Viterbi: phoneme level { } { } "i { l m ai ei n "ai Duration analysis Viterbi: word level a lion ai ei "ei i "i l "l m "m n "n a /@/ alien /"ei-li-@n/ lion /"lai-@n/ hmms Lexicon 13th May
67 Model implemented Stress feature extraction Feature extraction Viterbi: phoneme level { } { } "i l m ai ei n "ai } Viterbi: word level a lion ai ei "ei i "i l "l m "m n "n a /@/ alien /"ei-li-@n/ lion /"lai-@n/ hmms Lexicon 13th May
68 System sox Praat f o: "n "i: m I k t r A: n "s "k "r "I "p S n " z HCopy cvf HERest HCompV trained hmms HVite 13th May recognised words
69 System Hidden Markov Toolkit Corpus Gesproken Nederlands 772 recordings files words 53 hours 13th May
70 Time Using 6 to 8 computers: Training: 1 8 hours per iteration Evaluation: 4 10 hours per iteration 60 training iterations for 2 recognisers 13th May
71 Experimental set-up Conventional speech recogniser Stress-enabled speech recogniser 13th May
72 Results duration /"i:/ /i:/ th May
73 Results duration /"n/ /n/ th May
74 Results spectral tilt /"a:/ /a:/ th May
75 Results spectral tilt /"d/ /d/ th May
76 Results training 45 Recognition accuracy (%) Training iteration 13th May
77 Results Recognition improvement 13th May
78 Results Recognition improvement Conventional speech recogniser Stress-enabled speech recogniser Word error rate % A 2.6 % relative improvement. Word error rate % 13th May
79 Conclusion 13th May
80 Conclusion Using lexical stress in an automatic speech recogniser for continuous, large-vocabulary speech can improve the recognition rate. 13th May
81 Conclusion Using lexical stress in an automatic speech recogniser for continuous, large-vocabulary speech can improve the recognition rate. Consonants 13th May
82 Future work Model duration Model phrasal stress 13th May
83 13th May
Lecture 12: An Overview of Speech Recognition
Lecture : An Overview of peech Recognition. Introduction We can classify speech recognition tasks and systems along a set of dimensions that produce various tradeoffs in applicability and robustness. Isolated
Myanmar Continuous Speech Recognition System Based on DTW and HMM
Myanmar Continuous Speech Recognition System Based on DTW and HMM Ingyin Khaing Department of Information and Technology University of Technology (Yatanarpon Cyber City),near Pyin Oo Lwin, Myanmar Abstract-
Hidden Markov Model Toolkit (HTK) - Quick Start by David Philippou-Hübner
Hidden Markov Model Toolkit (HTK) - Quick Start by David Philippou-Hübner This 5 page step-by-step tutorial shows how to build a (minimal) automated speech recognizer using HTK. It is a compact version
BLIND SOURCE SEPARATION OF SPEECH AND BACKGROUND MUSIC FOR IMPROVED SPEECH RECOGNITION
BLIND SOURCE SEPARATION OF SPEECH AND BACKGROUND MUSIC FOR IMPROVED SPEECH RECOGNITION P. Vanroose Katholieke Universiteit Leuven, div. ESAT/PSI Kasteelpark Arenberg 10, B 3001 Heverlee, Belgium Peter.Vanroose@esat.kuleuven.ac.be
Hardware Implementation of Probabilistic State Machine for Word Recognition
IJECT Vo l. 4, Is s u e Sp l - 5, Ju l y - Se p t 2013 ISSN : 2230-7109 (Online) ISSN : 2230-9543 (Print) Hardware Implementation of Probabilistic State Machine for Word Recognition 1 Soorya Asokan, 2
Spot me if you can: Uncovering spoken phrases in encrypted VoIP conversations
Spot me if you can: Uncovering spoken phrases in encrypted VoIP conversations C. Wright, L. Ballard, S. Coull, F. Monrose, G. Masson Talk held by Goran Doychev Selected Topics in Information Security and
Speech Recognition on Cell Broadband Engine UCRL-PRES-223890
Speech Recognition on Cell Broadband Engine UCRL-PRES-223890 Yang Liu, Holger Jones, John Johnson, Sheila Vaidya (Lawrence Livermore National Laboratory) Michael Perrone, Borivoj Tydlitat, Ashwini Nanda
AUTOMATIC PHONEME SEGMENTATION WITH RELAXED TEXTUAL CONSTRAINTS
AUTOMATIC PHONEME SEGMENTATION WITH RELAXED TEXTUAL CONSTRAINTS PIERRE LANCHANTIN, ANDREW C. MORRIS, XAVIER RODET, CHRISTOPHE VEAUX Very high quality text-to-speech synthesis can be achieved by unit selection
Robust Methods for Automatic Transcription and Alignment of Speech Signals
Robust Methods for Automatic Transcription and Alignment of Speech Signals Leif Grönqvist (lgr@msi.vxu.se) Course in Speech Recognition January 2. 2004 Contents Contents 1 1 Introduction 2 2 Background
The Effects of Speaking Rate on Mandarin Tones. C2008 Yun H. Stockton
The Effects of Speaking Rate on Mandarin Tones BY C2008 Yun H. Stockton Submitted to the graduate degree program in Linguistics and the Graduate Faculty of the University of Kansas in partial fulfillment
Thirukkural - A Text-to-Speech Synthesis System
Thirukkural - A Text-to-Speech Synthesis System G. L. Jayavardhana Rama, A. G. Ramakrishnan, M Vijay Venkatesh, R. Murali Shankar Department of Electrical Engg, Indian Institute of Science, Bangalore 560012,
Ericsson T18s Voice Dialing Simulator
Ericsson T18s Voice Dialing Simulator Mauricio Aracena Kovacevic, Anna Dehlbom, Jakob Ekeberg, Guillaume Gariazzo, Eric Lästh and Vanessa Troncoso Dept. of Signals Sensors and Systems Royal Institute of
CHANWOO KIM (BIRTH: APR. 9, 1976) Language Technologies Institute School of Computer Science Aug. 8, 2005 present
CHANWOO KIM (BIRTH: APR. 9, 1976) 2602E NSH Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 Phone: +1-412-726-3996 Email: chanwook@cs.cmu.edu RESEARCH INTERESTS Speech recognition system,
Artificial Neural Network for Speech Recognition
Artificial Neural Network for Speech Recognition Austin Marshall March 3, 2005 2nd Annual Student Research Showcase Overview Presenting an Artificial Neural Network to recognize and classify speech Spoken
Using Words and Phonetic Strings for Efficient Information Retrieval from Imperfectly Transcribed Spoken Documents
Using Words and Phonetic Strings for Efficient Information Retrieval from Imperfectly Transcribed Spoken Documents Michael J. Witbrock and Alexander G. Hauptmann Carnegie Mellon University ABSTRACT Library
Using Tone Information in Thai Spelling Speech Recognition
Using Tone Information in Thai Spelling Speech Recognition Natthawut Kertkeidkachorn 1 Department of Computer Engineering, Chulalongkorn University Bangkok, Thailand 2 Department of Informatics The Graduate
A Segmentation Algorithm for Zebra Finch Song at the Note Level. Ping Du and Todd W. Troyer
A Segmentation Algorithm for Zebra Finch Song at the Note Level Ping Du and Todd W. Troyer Neuroscience and Cognitive Science Program, Dept. of Psychology University of Maryland, College Park, MD 20742
HTK Tutorial. Giampiero Salvi
Giampiero Salvi KTH (Royal Institute of Technology), Dep. of Speech, Music and Hearing, Drottning Kristinas v. 31, SE-100 44, Stockholm, Sweden giampi@speech.kth.se Giampiero Salvi, Nov. 2003 1/36 Outline
Testing Data-Driven Learning Algorithms for PoS Tagging of Icelandic
Testing Data-Driven Learning Algorithms for PoS Tagging of Icelandic by Sigrún Helgadóttir Abstract This paper gives the results of an experiment concerned with training three different taggers on tagged
Speech Recognition System of Arabic Alphabet Based on a Telephony Arabic Corpus
Speech Recognition System of Arabic Alphabet Based on a Telephony Arabic Corpus Yousef Ajami Alotaibi 1, Mansour Alghamdi 2, and Fahad Alotaiby 3 1 Computer Engineering Department, King Saud University,
Changes in Pilot Control Behaviour across Stewart Platform Motion Systems
Changes in Pilot Control Behaviour across Stewart Platform Motion Systems PROEFSCHRIFT ter verkrijging van de graad van doctor aan de Technische Universiteit Delft, op gezag van de Rector Magnificus prof.
Establishing the Uniqueness of the Human Voice for Security Applications
Proceedings of Student/Faculty Research Day, CSIS, Pace University, May 7th, 2004 Establishing the Uniqueness of the Human Voice for Security Applications Naresh P. Trilok, Sung-Hyuk Cha, and Charles C.
Speech recognition technology for mobile phones
Speech recognition technology for mobile phones Stefan Dobler Following the introduction of mobile phones using voice commands, speech recognition is becoming standard on mobile handsets. Features such
CONATION: English Command Input/Output System for Computers
CONATION: English Command Input/Output System for Computers Kamlesh Sharma* and Dr. T. V. Prasad** * Research Scholar, ** Professor & Head Dept. of Comp. Sc. & Engg., Lingaya s University, Faridabad, India
Barrister or solicitor?
Activity 1 Listening for gist The interviewer is talking to a secondary school Law teacher about why she didn t become a lawyer. What does she say about the differences between a barrister s work and a
Developing an Isolated Word Recognition System in MATLAB
MATLAB Digest Developing an Isolated Word Recognition System in MATLAB By Daryl Ning Speech-recognition technology is embedded in voice-activated routing systems at customer call centres, voice dialling
Turkish Radiology Dictation System
Turkish Radiology Dictation System Ebru Arısoy, Levent M. Arslan Boaziçi University, Electrical and Electronic Engineering Department, 34342, Bebek, stanbul, Turkey arisoyeb@boun.edu.tr, arslanle@boun.edu.tr
A Soft Computing Based Approach for Multi-Accent Classification in IVR Systems
A Soft Computing Based Approach for Multi-Accent Classification in IVR Systems by Sameeh Ullah A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of
Emotion in Speech: towards an integration of linguistic, paralinguistic and psychological analysis
Emotion in Speech: towards an integration of linguistic, paralinguistic and psychological analysis S-E.Fotinea 1, S.Bakamidis 1, T.Athanaselis 1, I.Dologlou 1, G.Carayannis 1, R.Cowie 2, E.Douglas-Cowie
Speech: A Challenge to Digital Signal Processing Technology for Human-to-Computer Interaction
: A Challenge to Digital Signal Processing Technology for Human-to-Computer Interaction Urmila Shrawankar Dept. of Information Technology Govt. Polytechnic, Nagpur Institute Sadar, Nagpur 440001 (INDIA)
Word Completion and Prediction in Hebrew
Experiments with Language Models for בס"ד Word Completion and Prediction in Hebrew 1 Yaakov HaCohen-Kerner, Asaf Applebaum, Jacob Bitterman Department of Computer Science Jerusalem College of Technology
Cursive Handwriting Recognition for Document Archiving
International Digital Archives Project Cursive Handwriting Recognition for Document Archiving Trish Keaton Rod Goodman California Institute of Technology Motivation Numerous documents have been conserved
ADVANCES IN ARABIC BROADCAST NEWS TRANSCRIPTION AT RWTH. David Rybach, Stefan Hahn, Christian Gollan, Ralf Schlüter, Hermann Ney
ADVANCES IN ARABIC BROADCAST NEWS TRANSCRIPTION AT RWTH David Rybach, Stefan Hahn, Christian Gollan, Ralf Schlüter, Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department,
Comp 14112 Fundamentals of Artificial Intelligence Lecture notes, 2015-16 Speech recognition
Comp 14112 Fundamentals of Artificial Intelligence Lecture notes, 2015-16 Speech recognition Tim Morris School of Computer Science, University of Manchester 1 Introduction to speech recognition 1.1 The
Audio Content Analysis for Online Audiovisual Data Segmentation and Classification
IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 9, NO. 4, MAY 2001 441 Audio Content Analysis for Online Audiovisual Data Segmentation and Classification Tong Zhang, Member, IEEE, and C.-C. Jay
Grammars and introduction to machine learning. Computers Playing Jeopardy! Course Stony Brook University
Grammars and introduction to machine learning Computers Playing Jeopardy! Course Stony Brook University Last class: grammars and parsing in Prolog Noun -> roller Verb thrills VP Verb NP S NP VP NP S VP
Unit 2 Title: Word Work Grade Level: 1 st Grade Timeframe: 6 Weeks
Unit 2 Title: Grade Level: 1 st Grade Timeframe: 6 Weeks Unit Overview: This unit of word work will focus on the student s ability to identify and pronounce the initial, medial vowel, and final sounds.
Efficient diphone database creation for MBROLA, a multilingual speech synthesiser
Efficient diphone database creation for, a multilingual speech synthesiser Institute of Linguistics Adam Mickiewicz University Poznań OWD 2010 Wisła-Kopydło, Poland Why? useful for testing speech models
Detecting Advertising in Radio using Machine Learning
Detecting Advertising in Radio using Machine Learning November 8, 2007 Robin Müller-Cajar rdc32@student.canterbury.ac.nz Department of Computer Science and Software Engineering University of Canterbury,
Automatic Evaluation Software for Contact Centre Agents voice Handling Performance
International Journal of Scientific and Research Publications, Volume 5, Issue 1, January 2015 1 Automatic Evaluation Software for Contact Centre Agents voice Handling Performance K.K.A. Nipuni N. Perera,
APPLYING MFCC-BASED AUTOMATIC SPEAKER RECOGNITION TO GSM AND FORENSIC DATA
APPLYING MFCC-BASED AUTOMATIC SPEAKER RECOGNITION TO GSM AND FORENSIC DATA Tuija Niemi-Laitinen*, Juhani Saastamoinen**, Tomi Kinnunen**, Pasi Fränti** *Crime Laboratory, NBI, Finland **Dept. of Computer
Video Affective Content Recognition Based on Genetic Algorithm Combined HMM
Video Affective Content Recognition Based on Genetic Algorithm Combined HMM Kai Sun and Junqing Yu Computer College of Science & Technology, Huazhong University of Science & Technology, Wuhan 430074, China
EVALITA-ISTC Comparison of Open Source Tools on Clean and Noisy Digits Recognition Tasks
EVALITA-ISTC Comparison of Open Source Tools on Clean and Noisy Digits Recognition Tasks Piero Cosi Mauro Nicolao ISTITUTO DI SCIENZE E TECNOLOGIE DELLA COGNIZIONE SEDE DI PADOVA - FONETICA E DIALETTOLOGIA
Membering T M : A Conference Call Service with Speaker-Independent Name Dialing on AIN
PAGE 30 Membering T M : A Conference Call Service with Speaker-Independent Name Dialing on AIN Sung-Joon Park, Kyung-Ae Jang, Jae-In Kim, Myoung-Wan Koo, Chu-Shik Jhon Service Development Laboratory, KT,
Phonemic Awareness. Section III
Section III Phonemic Awareness Rationale Without knowledge of the separate sounds that make up words, it is difficult for children to hear separate sounds, recognize the sound s position in a word, and
Unit 1 Title: Word Work Grade Level: 1 st Grade Timeframe: 6 Weeks
Unit 1 Title: Grade Level: 1 st Grade Timeframe: 6 Weeks Unit Overview: This unit of word work will focus on the student s ability to distinguish long and short vowel sounds in single syllable Students
http://www.guido.be/intranet/enqueteoverview/tabid/152/ctl/eresults...
1 van 70 20/03/2014 11:55 EnqueteDescription 2 van 70 20/03/2014 11:55 3 van 70 20/03/2014 11:55 4 van 70 20/03/2014 11:55 5 van 70 20/03/2014 11:55 6 van 70 20/03/2014 11:55 7 van 70 20/03/2014 11:55
School Class Monitoring System Based on Audio Signal Processing
C. R. Rashmi 1,,C.P.Shantala 2 andt.r.yashavanth 3 1 Department of CSE, PG Student, CIT, Gubbi, Tumkur, Karnataka, India. 2 Department of CSE, Vice Principal & HOD, CIT, Gubbi, Tumkur, Karnataka, India.
Emotion Detection from Speech
Emotion Detection from Speech 1. Introduction Although emotion detection from speech is a relatively new field of research, it has many potential applications. In human-computer or human-human interaction
Using Segmentation Constraints in an Implicit Segmentation Scheme for Online Word Recognition
1 Using Segmentation Constraints in an Implicit Segmentation Scheme for Online Word Recognition Émilie CAILLAULT LASL, ULCO/University of Littoral (Calais) Christian VIARD-GAUDIN IRCCyN, University of
Sign Language Phoneme Transcription with Rule-based Hand Trajectory Segmentation
J Sign Process Syst (2010) 59:211 222 DOI 10.1007/s11265-008-0292-5 Sign Language Phoneme Transcription with Rule-based Hand Trajectory Segmentation W. W. Kong Surendra Ranganath Received: 21 May 2008
ACOUSTICAL CONSIDERATIONS FOR EFFECTIVE EMERGENCY ALARM SYSTEMS IN AN INDUSTRIAL SETTING
ACOUSTICAL CONSIDERATIONS FOR EFFECTIVE EMERGENCY ALARM SYSTEMS IN AN INDUSTRIAL SETTING Dennis P. Driscoll, P.E. and David C. Byrne, CCC-A Associates in Acoustics, Inc. Evergreen, Colorado Telephone (303)
Framework for Joint Recognition of Pronounced and Spelled Proper Names
Framework for Joint Recognition of Pronounced and Spelled Proper Names by Atiwong Suchato B.S. Electrical Engineering, (1998) Chulalongkorn University Submitted to the Department of Electrical Engineering
Technologies for Voice Portal Platform
Technologies for Voice Portal Platform V Yasushi Yamazaki V Hitoshi Iwamida V Kazuhiro Watanabe (Manuscript received November 28, 2003) The voice user interface is an important tool for realizing natural,
Principles of Instruction. Teaching Letter-Sound Associations by Rebecca Felton, PhD. Introduction
Teaching Letter-Sound Associations by Rebecca Felton, PhD. Introduction In order to become proficient readers, students must learn to recognize and name the letters of the English alphabet. Students must
Automated Content Analysis of Discussion Transcripts
Automated Content Analysis of Discussion Transcripts Vitomir Kovanović v.kovanovic@ed.ac.uk Dragan Gašević dgasevic@acm.org School of Informatics, University of Edinburgh Edinburgh, United Kingdom v.kovanovic@ed.ac.uk
IMPROVING TTS BY HIGHER AGREEMENT BETWEEN PREDICTED VERSUS OBSERVED PRONUNCIATIONS
IMPROVING TTS BY HIGHER AGREEMENT BETWEEN PREDICTED VERSUS OBSERVED PRONUNCIATIONS Yeon-Jun Kim, Ann Syrdal AT&T Labs-Research, 180 Park Ave. Florham Park, NJ 07932 Matthias Jilka Institut für Linguistik,
Intonation and information structure (part II)
Lecture 4 Intonation and information structure (part II) CN MSc course Language Acquisition, 22 March 2011 Instructor: Aoju Chen 1 Topic and focus in sentence initial position 100% 80% 60% 40% 20% 0% H*
Unlocking Value from. Patanjali V, Lead Data Scientist, Tiger Analytics Anand B, Director Analytics Consulting,Tiger Analytics
Unlocking Value from Patanjali V, Lead Data Scientist, Anand B, Director Analytics Consulting, EXECUTIVE SUMMARY Today a lot of unstructured data is being generated in the form of text, images, videos
Useful classroom language for Elementary students. (Fluorescent) light
Useful classroom language for Elementary students Classroom objects it is useful to know the words for Stationery Board pens (= board markers) Rubber (= eraser) Automatic pencil Lever arch file Sellotape
Speech and Language Processing
Speech and Language Processing An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition Second Edition Daniel Jurafsky Stanford University James H. Martin University
OCPS Curriculum, Instruction, Assessment Alignment
OCPS Curriculum, Instruction, Assessment Alignment Subject Area: Grade: Strand 1: Standard 1: Reading and Language Arts Kindergarten Reading Process The student demonstrates knowledge of the concept of
THE RWTH ENGLISH LECTURE RECOGNITION SYSTEM
THE RWTH ENGLISH LECTURE RECOGNITION SYSTEM Simon Wiesler 1, Kazuki Irie 2,, Zoltán Tüske 1, Ralf Schlüter 1, Hermann Ney 1,2 1 Human Language Technology and Pattern Recognition, Computer Science Department,
German Speech Recognition: A Solution for the Analysis and Processing of Lecture Recordings
German Speech Recognition: A Solution for the Analysis and Processing of Lecture Recordings Haojin Yang, Christoph Oehlke, Christoph Meinel Hasso Plattner Institut (HPI), University of Potsdam P.O. Box
Developing acoustics models for automatic speech recognition
Developing acoustics models for automatic speech recognition GIAMPIERO SALVI Master s Thesis at TMH Supervisor: Håkan Melin Examiner: Rolf Carlson TRITA xxx yyyy-nn iii Abstract This thesis is concerned
Experiments with Signal-Driven Symbolic Prosody for Statistical Parametric Speech Synthesis
Experiments with Signal-Driven Symbolic Prosody for Statistical Parametric Speech Synthesis Fabio Tesser, Giacomo Sommavilla, Giulio Paci, Piero Cosi Institute of Cognitive Sciences and Technologies, National
Recent advances in Digital Music Processing and Indexing
Recent advances in Digital Music Processing and Indexing Acoustics 08 warm-up TELECOM ParisTech Gaël RICHARD Telecom ParisTech (ENST) www.enst.fr/~grichard/ Content Introduction and Applications Components
Statistical text-to-speech synthesis of Spanish subtitles
Statistical text-to-speech synthesis of Spanish subtitles S. Piqueras, M. A. del-agua, A. Giménez, J. Civera, and A. Juan MLLP, DSIC, Universitat Politècnica de València, Camí de Vera s/n, 46022, València,
Common Core State Standards English Language Arts. IEP Goals and Objectives Guidance: Basic Format
Current Academic Achievement Common Core State Standards English Language Arts IEP Goals and Objectives Guidance: Basic Format Just as you have, address a deficit by stating what the student can do relative
Connected Digits Recognition Task: ISTC CNR Comparison of Open Source Tools
Connected Digits Recognition Task: ISTC CNR Comparison of Open Source Tools Piero Cosi, Mauro Nicolao Istituto di Scienze e Tecnologie della Cognizione, C.N.R. via Martiri della libertà, 2 35137 Padova
Structural Health Monitoring Tools (SHMTools)
Structural Health Monitoring Tools (SHMTools) Parameter Specifications LANL/UCSD Engineering Institute LA-CC-14-046 c Copyright 2014, Los Alamos National Security, LLC All rights reserved. May 30, 2014
Annotated bibliographies for presentations in MUMT 611, Winter 2006
Stephen Sinclair Music Technology Area, McGill University. Montreal, Canada Annotated bibliographies for presentations in MUMT 611, Winter 2006 Presentation 4: Musical Genre Similarity Aucouturier, J.-J.
DRA2 Word Analysis. correlated to. Virginia Learning Standards Grade 1
DRA2 Word Analysis correlated to Virginia Learning Standards Grade 1 Quickly identify and generate words that rhyme with given words. Quickly identify and generate words that begin with the same sound.
Running head: PROCESSING REDUCED WORD FORMS. Processing reduced word forms: the suffix restoration effect. Rachèl J.J.K. Kemps, Mirjam Ernestus
Running head: PROCESSING REDUCED WORD FORMS Processing reduced word forms: the suffix restoration effect Rachèl J.J.K. Kemps, Mirjam Ernestus Max Planck Institute for Psycholinguistics Nijmegen, The Netherlands
Comprehensive Reading Assessment Grades K-1
Comprehensive Reading Assessment Grades K-1 User Information Name: Doe, John Date of Birth: Jan 01, 1995 Current Grade in School: 3rd Grade in School at Evaluation: 1st Evaluation Date: May 17, 2006 Background
Information Leakage in Encrypted Network Traffic
Information Leakage in Encrypted Network Traffic Attacks and Countermeasures Scott Coull RedJack Joint work with: Charles Wright (MIT LL) Lucas Ballard (Google) Fabian Monrose (UNC) Gerald Masson (JHU)
Probabilistic topic models for sentiment analysis on the Web
University of Exeter Department of Computer Science Probabilistic topic models for sentiment analysis on the Web Chenghua Lin September 2011 Submitted by Chenghua Lin, to the the University of Exeter as
An Arabic Text-To-Speech System Based on Artificial Neural Networks
Journal of Computer Science 5 (3): 207-213, 2009 ISSN 1549-3636 2009 Science Publications An Arabic Text-To-Speech System Based on Artificial Neural Networks Ghadeer Al-Said and Moussa Abdallah Department
2013 2015 M.A. Hispanic Linguistics. University of Arizona: Tucson, AZ.
M I G U E L L L O M PA R T G A R C I A University of Arizona Department of Spanish and portuguese Modern Languages 550 Tucson, AZ 85721 Phone: +34 639068540 Fax: Email: mllompart@email.arizona.edu Homepage:
Grade 1 LA. 1. 1. 1. 1. Subject Grade Strand Standard Benchmark. Florida K-12 Reading and Language Arts Standards 27
Grade 1 LA. 1. 1. 1. 1 Subject Grade Strand Standard Benchmark Florida K-12 Reading and Language Arts Standards 27 Grade 1: Reading Process Concepts of Print Standard: The student demonstrates knowledge
Available from Deakin Research Online:
This is the authors final peered reviewed (post print) version of the item published as: Adibi,S 2014, A low overhead scaled equalized harmonic-based voice authentication system, Telematics and informatics,
SWING: A tool for modelling intonational varieties of Swedish Beskow, Jonas; Bruce, Gösta; Enflo, Laura; Granström, Björn; Schötz, Susanne
SWING: A tool for modelling intonational varieties of Swedish Beskow, Jonas; Bruce, Gösta; Enflo, Laura; Granström, Björn; Schötz, Susanne Published in: Proceedings of Fonetik 2008 Published: 2008-01-01
CURRICULUM VITAE. Toby Macrae, Ph.D., CCC-SLP
CURRICULUM VITAE Toby Macrae, Ph.D., CCC-SLP Assistant Professor School of Communication Science and Disorders Florida State University 201 W. Bloxham Street Tallahassee, Florida 32306-1200 toby.macrae@cci.fsu.edu
Congenitally Deaf Children Generate Iconic Vocalizations to Communicate Magnitude
Congenitally Deaf Children Generate Iconic Vocalizations to Communicate Magnitude Marcus Perlman (mperlman@wisc.edu) Department of Psychology, 1202 W. Johnson Street University of Wisconsin-Madison Madison,
Online Diarization of Telephone Conversations
Odyssey 2 The Speaker and Language Recognition Workshop 28 June July 2, Brno, Czech Republic Online Diarization of Telephone Conversations Oshry Ben-Harush, Itshak Lapidot, Hugo Guterman Department of
Hidden Markov Models
8.47 Introduction to omputational Molecular Biology Lecture 7: November 4, 2004 Scribe: Han-Pang hiu Lecturer: Ross Lippert Editor: Russ ox Hidden Markov Models The G island phenomenon The nucleotide frequencies
CHARTES D'ANGLAIS SOMMAIRE. CHARTE NIVEAU A1 Pages 2-4. CHARTE NIVEAU A2 Pages 5-7. CHARTE NIVEAU B1 Pages 8-10. CHARTE NIVEAU B2 Pages 11-14
CHARTES D'ANGLAIS SOMMAIRE CHARTE NIVEAU A1 Pages 2-4 CHARTE NIVEAU A2 Pages 5-7 CHARTE NIVEAU B1 Pages 8-10 CHARTE NIVEAU B2 Pages 11-14 CHARTE NIVEAU C1 Pages 15-17 MAJ, le 11 juin 2014 A1 Skills-based
ACKNOWLEDGEMENTS. At the completion of this study there are many people that I need to thank. Foremost of
ACKNOWLEDGEMENTS At the completion of this study there are many people that I need to thank. Foremost of these are John McCarthy. He has been a wonderful mentor and advisor. I also owe much to the other
Teacher training worksheets- Classroom language Pictionary miming definitions game Worksheet 1- General school vocab version
Teacher training worksheets- Classroom language Pictionary miming definitions game Worksheet 1- General school vocab version Whiteboard Work in pairs Desk Board pen Permanent marker Felt tip pen Colouring
Developing IEP Goals & Objectives
Developing IEP Goals & Objectives Middletown Department of Student Services Parent Partnership Robert Dunn, Amanda Lamoglia & Heather Mills-Pevonis Overview & Objectives Welcome & Introductions Objective:
2e. Initial sounds isolate and pronounce in CVC (/c/ in cat) Onsets and rhymes in single
Kindergarten Common Core ELA Foundational Skills Unit Timeline Trimester 1 Trimester 2 1a. Follow print left to right top to bottom page by page 1b. / 1c.Words recognize letters together in a specific
have more skill and perform more complex
Speech Recognition Smartphone UI Speech Recognition Technology and Applications for Improving Terminal Functionality and Service Usability User interfaces that utilize voice input on compact devices such
Speech Technology Project 2004 Building an HMM Speech Recogniser for Dutch
Speech Technology Project 2004 Building an HMM Speech Recogniser for Dutch Frans Adriaans Markus Heukelom Marijn Koolen Tom Lentz Ork de Rooij Daan Vreeswijk Supervision: Rob van Son 9th July 2004 Contents
THE VOICE OF LOVE. Trisha Belanger, Caroline Menezes, Claire Barboa, Mofida Helo, Kimia Shirazifard
THE VOICE OF LOVE Trisha Belanger, Caroline Menezes, Claire Barboa, Mofida Helo, Kimia Shirazifard University of Toledo, United States tbelanger@rockets.utoledo.edu, Caroline.Menezes@utoledo.edu, Claire.Barbao@rockets.utoledo.edu,
ICFHR 2010 Tutorial: Multimodal Computer Assisted Transcription of Handwriting Images
ICFHR 2010 Tutorial: Multimodal Computer Assisted Transcription of Handwriting Images III Multimodality in Computer Assisted Transcription Alejandro H. Toselli & Moisés Pastor & Verónica Romero {ahector,moises,vromero}@iti.upv.es
Quarterly Progress and Status Report. Word stress and duration in Finnish
Dept. for Speech, Music and Hearing Quarterly Progress and Status Report Word stress and duration in Finnish Suomi, K. journal: Proceedings of Fonetik, TMH-QPSR volume: 44 number: 1 year: 2002 pages: 073-076
INTEGRATING THE COMMON CORE STANDARDS INTO INTERACTIVE, ONLINE EARLY LITERACY PROGRAMS
INTEGRATING THE COMMON CORE STANDARDS INTO INTERACTIVE, ONLINE EARLY LITERACY PROGRAMS By Dr. Kay MacPhee President/Founder Ooka Island, Inc. 1 Integrating the Common Core Standards into Interactive, Online
Text-To-Speech Technologies for Mobile Telephony Services
Text-To-Speech Technologies for Mobile Telephony Services Paulseph-John Farrugia Department of Computer Science and AI, University of Malta Abstract. Text-To-Speech (TTS) systems aim to transform arbitrary
Modeling Cantonese Pronunciation Variations for Large-Vocabulary Continuous Speech Recognition
Computational Linguistics and Chinese Language Processing Vol. 11, No. 1, March 2006, pp. 17-36 17 The Association for Computational Linguistics and Chinese Language Processing Modeling Cantonese Pronunciation
B. Raghavendhar Reddy #1, E. Mahender *2
Speech to Text Conversion using Android Platform B. Raghavendhar Reddy #1, E. Mahender *2 #1 Department of Electronics Communication and Engineering Aurora s Technological and Research Institute Parvathapur,