|
|
- Douglas Manning
- 5 years ago
- Views:
Transcription
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21 Sensors Utterance-Context Pair utterance linguistic unit 1 linguistic unit 2 linguistic unit M semantic catregory 1 semantic category N context semantic category 2
22 utterance linguistic unit prototype linguistic unit prototype linguistic unit prototype context semantic category prototype semantic category prototype semantic category prototype
23 linguistic unit prototype linguistic unit prototype linguistic unit prototype semantic category prototype semantic category prototype semantic category prototype semantic category prototype semantic category prototype linguistic unit prototype linguistic unit prototype linguistic unit prototype semantic category prototype semantic category prototype semantic category prototype Lexicon semantic category linguistic unit semantic category linguistic unit linguistic unit prototype linguistic unit prototype
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50 Lexical Items Long Term Memory (LTM) Mutual Information filter Lexical Candidates Mid Term Memory (MTM) Recurrence filter Short Term Memory (STM) Linguistic-Semantic Events (LS-events) Co-occurence filter Linguistic Events (L-events) Semantic Events (S-events) Event Detection Linguistic Channels Contextual Channels Input Sensor Signals Feature Extraction time
51
52
53
54
55 feature analyzers sensors input signals Linguistic channels time Contextual channels time
56
57 linguistic channels linguistic event detector L-events contextual channels semantic event detector S-events time time
58 segment 1 segment 2 segment n channel 1 channel 2 channel 3 An L-event or S-event is composed of multiple channels Event Segmenter The event is divided into an array along channel and time segment boundaries. channel 1 channel 2 channel 3
59 An event divided along time segments and channels Some potential subevents
60 linguistic events (L-events) time semantic events (S-events) co-occuring L-events and S-events are paired to form LS-events L-event S-event L-event L-event L-event L-event S-event S-event S-event S-event old LS-events forgotten short term memory (STM) contains recent LS-events
61
62
63 /* Consider all pairs of LS-events in short term memory */ for each pair of LS-events in STM, LS i and LS j { } /* Compare each pair of L-subevents in LS i and LS j */ for each L-subevent in LS i, L i { for each L-subevent in LS j, L j { if d L (L i, L j ) < t L then set L match = TRUE } } /* Compare each pair of S-subevents in LS i and LS j */ for each S-subevent in LS i, S i { for each S-subevent in LS j, S j { if d S (S i, S j ) < t S then set S match = TRUE } } /* check for matches of L-subevents and co-occuring S-subevents */ if L match = TRUE and S match = TRUE then recurrent match found }
64 LS-event Short Term Memory (STM) LS-event LS-event Filled regions indicate recurrent L-subevents and S-subevents linguistic unit prototype ( ) semantic prototype ( ) Mid Term Memory (MTM) Lexical Candidate
65 L-unit = {, } S-category = {, } L-radius ( ) S-radius ( ) linguistic feature space L-prototype ( ) S-prototype ( ) contextual feature space
66
67
68
69
70 medium large c l o a d j h g e m p b k n u t s r z y x w v i f q c o a d g e p b k n u y w v c o a d g e p b k n u y w v c o a d g e m p b k n u s y x w v c o a d g e m p b k n u s y w v x c o a d j h g e m p b k n u t s r z y x w v l s r l i j h f m t q z x l i j h f m t s r q z x l i j h f t r q z i l j h f t r q z i f q small I(S;L) = bits I(S;L) = 0.29 bits I(S;L) = 0.0 bits
71 Mid Term Memory (MTM) Mutual Information Filter Lexical Item
72
73
74 LTM Sensors Feature analysis L-event detection Event segmentation L-event L-subevent matches L-unit in lexical item S-category of recognized L-unit LTM Sensors Feature analysis S-event detection Event segmentation S-event S-subevent matches S-category in lexical item L-unit of recognized S-category
75 Sensors Feature analysis Event detection Event segmentation STM LS-prototype hypotheses Lexical search Recurrence filter MTM Mutual information filter LTM matched hypotheses explained away
76
77 Lexical item i Lexical item j L-unit S-category L-units and S-categories overlap Matching lexical items are clustered to form a conglomerate lexical item
78 Lexical item i Lexical item j L-unit S-category S-prototype i matches S-category j Lexical item i Lexical item j S-category L-unit L-prototype j matches L-unit i
79 Linguistic Units Semantic Categories
80
81 Goals MTM mutual information filter LTM Action selection Environment threshold adjustment lexical item confidence adjustment Feedback
82
83
84
85 Long Term Memory Lexical items: {spoken word model, color/shape category} Mutual information filter Lexical candidates: {spoken word prototype, color/shape prototype} Mid Term Memory Recurrence filter Short Term Memory LS-events: {spoken utterance, object view-set} Co-occurence filter L-subevents: speech segments L-events: spoken utterances Linguistic channel: phoneme probabilities L-event unpacking Spoken utterance detection S-event unpacking Object detection S-subevents: shape / color view-sets S-events: object view-sets Contextual channels: object shape & color Phoneme analysis Object shape analysis Object color analysis Microphone Camera
86
87
88 CCD camera color image foreground segmentation Context Channel 1: Shape mask-edge spatial derivative analysis Context Channel 2: Color foreground bitmap connected regions analysis object mask masked color image
89
90
91
92 Original RGB Image Color Histogram Object mask Shape histogram normalized green relative angle normalized red normalized distance
93 DOF 3: Neck rotation Color CCD Camera DOF 2: Base elevation DOF 4: Neck elevation DOF 1: Base rotation DOF 5: Object turntable rotation
94
95
96 12 units aa ae ah aw ay b RASTA-PLP spectral analysis 176 units 40 units ch d dh dx eh er ey fg hh ih iy jh kl m n ng ow oy pq 176 units r s sh sil t th uh uw vw time delay y z Recurrent Neural Network Linguistic channel: phoneme probabilities
97
98
99
100 aa ae ah aw ay b ch d dh dx eh er ey fg hh ih iy jh kl m n ng ow oy pq r s sh sil t th uh uw vw y z aa ae ah aw ay b ch d dh dx eh er ey fg hh ih iy jh kl m n ng ow oy pq r s sh sil t th uh uw vw y z aa ae ah aw ay b ch d dh dx eh er ey f g hh ih iy jh k l m n ng ow oy p q r s sh sil t th uh uw v w y z
101
102
103 state = 1; count_2 = 0; count_3 = 0; count_4 = 0 UTTERANCE_START_DELAY = 50ms; UTTERANCE_END_DELAY = 300ms for each RNN output vector, l(t) { state 1: SILENCE if SIL!= 1 { utterancestartindex = t state=2 } else { state = 1 } state 2: POSSIBLE_START_OF_UTTERANCE count_2 = count_2 + 1 if SIL = 1 { count_2 = 0 state = 1 } else if {count_2 > UTTERANCE_START_DELAY) { state = 3 } state 3: UTTERANCE if SIL { state = 4 } else { count_3 = count_3 + 1 state = 3 } } state 4: POSSIBLE_END_OF_UTTERANCE count_4 = count_4 + 1 if SIL!= 1 { count_3 = count_3 + count_4 count_4 = 0 state = 3 } } else if count_4 > UTTERANCE_END_DELAY { utteranceendindex = t - count_4-1 ProcessUtterance(utteranceStartIndex, utteranceendindex) count_2 = 0 count_3 = 0 count_4 = 0 state = 1 } }
104
105 a utterance start null b null utterance end silence
106
107 aa ae ah aw ay b ch d dh dx eh er ey fg hh ih iy jh kl m n ng ow oy pq r s sh sil t th uh uw vw y z RNN output phoneme probabilities Viterbi algorithm / b aa l / Most likely phoneme sequence b aa l Hidden Markov Model
108
109
110
111
112 "yeah" "dog" Mutual Information Mutual Information L-radius S-radius L-radius S-radius
113
114
115
116
117
118
119
120
121
122
123 Histogram bin occupancy (normalized) Distance between view-sets
124 Histogram bin occupancy (normalized) Distance between view-sets Histogram bin occupancy (normalized) Distance between view-sets
125
126
127
128
129
130
131
132
133
134
135 % CELL 7% Acoustic Recurrency CELL 72% 31% Acoustic Recurrency
136 % CELL 13% Acoustic Recurrency
137
138
139
140
141
142
143
144
145
146 Spoken commands CELL User-dependent acoustic & semantic model Task semantics
147
148
149 Scene 1: User points to three colors in the rainbow and names them (lexical acquisition) Scene 2: User selects a part from the "Tree of Life" by pointing to the part Scene 3: Part is colored by speech using one of the three lexical items learned in Scene 1 Scene 4: User must select position for new body part using gesture, confirm with speech Scene 5: A successfully placed part Scene 6: After two more cycles of Scenes 2-5 the mate is complete and Toco looks on in new-found love
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
On the Formation of Phoneme Categories in DNN Acoustic Models
On the Formation of Phoneme Categories in DNN Acoustic Models Tasha Nagamine Department of Electrical Engineering, Columbia University T. Nagamine Motivation Large performance gap between humans and state-
More informationLecture 9: Speech Recognition
EE E6820: Speech & Audio Processing & Recognition Lecture 9: Speech Recognition 1 Recognizing speech 2 Feature calculation Dan Ellis Michael Mandel 3 Sequence
More informationCharacterizing and Processing Robot-Directed Speech
Characterizing and Processing Robot-Directed Speech Paulina Varchavskaia, Paul Fitzpatrick, Cynthia Breazeal AI Lab, MIT, Cambridge, USA [paulina,paulfitz,cynthia]@ai.mit.edu Abstract. Speech directed
More informationSpeech Recognition at ICSI: Broadcast News and beyond
Speech Recognition at ICSI: Broadcast News and beyond Dan Ellis International Computer Science Institute, Berkeley CA Outline 1 2 3 The DARPA Broadcast News task Aspects of ICSI
More informationLearning Methods in Multilingual Speech Recognition
Learning Methods in Multilingual Speech Recognition Hui Lin Department of Electrical Engineering University of Washington Seattle, WA 98125 linhui@u.washington.edu Li Deng, Jasha Droppo, Dong Yu, and Alex
More informationA Neural Network GUI Tested on Text-To-Phoneme Mapping
A Neural Network GUI Tested on Text-To-Phoneme Mapping MAARTEN TROMPPER Universiteit Utrecht m.f.a.trompper@students.uu.nl Abstract Text-to-phoneme (T2P) mapping is a necessary step in any speech synthesis
More informationSpeech Segmentation Using Probabilistic Phonetic Feature Hierarchy and Support Vector Machines
Speech Segmentation Using Probabilistic Phonetic Feature Hierarchy and Support Vector Machines Amit Juneja and Carol Espy-Wilson Department of Electrical and Computer Engineering University of Maryland,
More informationLip reading: Japanese vowel recognition by tracking temporal changes of lip shape
Lip reading: Japanese vowel recognition by tracking temporal changes of lip shape Koshi Odagiri 1, and Yoichi Muraoka 1 1 Graduate School of Fundamental/Computer Science and Engineering, Waseda University,
More informationSpeech Recognition using Acoustic Landmarks and Binary Phonetic Feature Classifiers
Speech Recognition using Acoustic Landmarks and Binary Phonetic Feature Classifiers October 31, 2003 Amit Juneja Department of Electrical and Computer Engineering University of Maryland, College Park,
More informationModule 12. Machine Learning. Version 2 CSE IIT, Kharagpur
Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should
More informationSpeech Emotion Recognition Using Support Vector Machine
Speech Emotion Recognition Using Support Vector Machine Yixiong Pan, Peipei Shen and Liping Shen Department of Computer Technology Shanghai JiaoTong University, Shanghai, China panyixiong@sjtu.edu.cn,
More informationWord Segmentation of Off-line Handwritten Documents
Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department
More informationADVANCES IN DEEP NEURAL NETWORK APPROACHES TO SPEAKER RECOGNITION
ADVANCES IN DEEP NEURAL NETWORK APPROACHES TO SPEAKER RECOGNITION Mitchell McLaren 1, Yun Lei 1, Luciana Ferrer 2 1 Speech Technology and Research Laboratory, SRI International, California, USA 2 Departamento
More informationBODY LANGUAGE ANIMATION SYNTHESIS FROM PROSODY AN HONORS THESIS SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE OF STANFORD UNIVERSITY
BODY LANGUAGE ANIMATION SYNTHESIS FROM PROSODY AN HONORS THESIS SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE OF STANFORD UNIVERSITY Sergey Levine Principal Adviser: Vladlen Koltun Secondary Adviser:
More informationAUTOMATIC DETECTION OF PROLONGED FRICATIVE PHONEMES WITH THE HIDDEN MARKOV MODELS APPROACH 1. INTRODUCTION
JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 11/2007, ISSN 1642-6037 Marek WIŚNIEWSKI *, Wiesława KUNISZYK-JÓŹKOWIAK *, Elżbieta SMOŁKA *, Waldemar SUSZYŃSKI * HMM, recognition, speech, disorders
More informationFramewise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures
Framewise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures Alex Graves and Jürgen Schmidhuber IDSIA, Galleria 2, 6928 Manno-Lugano, Switzerland TU Munich, Boltzmannstr.
More informationA Comparison of DHMM and DTW for Isolated Digits Recognition System of Arabic Language
A Comparison of DHMM and DTW for Isolated Digits Recognition System of Arabic Language Z.HACHKAR 1,3, A. FARCHI 2, B.MOUNIR 1, J. EL ABBADI 3 1 Ecole Supérieure de Technologie, Safi, Morocco. zhachkar2000@yahoo.fr.
More informationModeling function word errors in DNN-HMM based LVCSR systems
Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford
More informationCourse Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE
EE-589 Introduction to Neural Assistant Prof. Dr. Turgay IBRIKCI Room # 305 (322) 338 6868 / 139 Wensdays 9:00-12:00 Course Outline The course is divided in two parts: theory and practice. 1. Theory covers
More informationOn Human Computer Interaction, HCI. Dr. Saif al Zahir Electrical and Computer Engineering Department UBC
On Human Computer Interaction, HCI Dr. Saif al Zahir Electrical and Computer Engineering Department UBC Human Computer Interaction HCI HCI is the study of people, computer technology, and the ways these
More informationHuman Emotion Recognition From Speech
RESEARCH ARTICLE OPEN ACCESS Human Emotion Recognition From Speech Miss. Aparna P. Wanare*, Prof. Shankar N. Dandare *(Department of Electronics & Telecommunication Engineering, Sant Gadge Baba Amravati
More informationLEGO MINDSTORMS Education EV3 Coding Activities
LEGO MINDSTORMS Education EV3 Coding Activities s t e e h s k r o W t n e d Stu LEGOeducation.com/MINDSTORMS Contents ACTIVITY 1 Performing a Three Point Turn 3-6 ACTIVITY 2 Written Instructions for a
More informationSpeaker recognition using universal background model on YOHO database
Aalborg University Master Thesis project Speaker recognition using universal background model on YOHO database Author: Alexandre Majetniak Supervisor: Zheng-Hua Tan May 31, 2011 The Faculties of Engineering,
More informationBooks Effective Literacy Y5-8 Learning Through Talk Y4-8 Switch onto Spelling Spelling Under Scrutiny
By the End of Year 8 All Essential words lists 1-7 290 words Commonly Misspelt Words-55 working out more complex, irregular, and/or ambiguous words by using strategies such as inferring the unknown from
More informationThe ABCs of O-G. Materials Catalog. Skills Workbook. Lesson Plans for Teaching The Orton-Gillingham Approach in Reading and Spelling
2008 Intermediate Level Skills Workbook Group 2 Groups 1 & 2 The ABCs of O-G The Flynn System by Emi Flynn Lesson Plans for Teaching The Orton-Gillingham Approach in Reading and Spelling The ABCs of O-G
More informationModeling function word errors in DNN-HMM based LVCSR systems
Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford
More informationAnalysis of Speech Recognition Models for Real Time Captioning and Post Lecture Transcription
Analysis of Speech Recognition Models for Real Time Captioning and Post Lecture Transcription Wilny Wilson.P M.Tech Computer Science Student Thejus Engineering College Thrissur, India. Sindhu.S Computer
More informationEffect of Word Complexity on L2 Vocabulary Learning
Effect of Word Complexity on L2 Vocabulary Learning Kevin Dela Rosa Language Technologies Institute Carnegie Mellon University 5000 Forbes Ave. Pittsburgh, PA kdelaros@cs.cmu.edu Maxine Eskenazi Language
More informationInternational Journal of Advanced Networking Applications (IJANA) ISSN No. :
International Journal of Advanced Networking Applications (IJANA) ISSN No. : 0975-0290 34 A Review on Dysarthric Speech Recognition Megha Rughani Department of Electronics and Communication, Marwadi Educational
More informationClass-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification
Class-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification Tomi Kinnunen and Ismo Kärkkäinen University of Joensuu, Department of Computer Science, P.O. Box 111, 80101 JOENSUU,
More informationA New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation
A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation SLSP-2016 October 11-12 Natalia Tomashenko 1,2,3 natalia.tomashenko@univ-lemans.fr Yuri Khokhlov 3 khokhlov@speechpro.com Yannick
More informationMulti-modal Sensing and Analysis of Poster Conversations toward Smart Posterboard
Multi-modal Sensing and Analysis of Poster Conversations toward Smart Posterboard Tatsuya Kawahara Kyoto University, Academic Center for Computing and Media Studies Sakyo-ku, Kyoto 606-8501, Japan http://www.ar.media.kyoto-u.ac.jp/crest/
More informationLikelihood-Maximizing Beamforming for Robust Hands-Free Speech Recognition
MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Likelihood-Maximizing Beamforming for Robust Hands-Free Speech Recognition Seltzer, M.L.; Raj, B.; Stern, R.M. TR2004-088 December 2004 Abstract
More informationNatural Language Processing. George Konidaris
Natural Language Processing George Konidaris gdk@cs.brown.edu Fall 2017 Natural Language Processing Understanding spoken/written sentences in a natural language. Major area of research in AI. Why? Humans
More informationAutoregressive product of multi-frame predictions can improve the accuracy of hybrid models
Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models Navdeep Jaitly 1, Vincent Vanhoucke 2, Geoffrey Hinton 1,2 1 University of Toronto 2 Google Inc. ndjaitly@cs.toronto.edu,
More informationDesign Of An Automatic Speaker Recognition System Using MFCC, Vector Quantization And LBG Algorithm
Design Of An Automatic Speaker Recognition System Using MFCC, Vector Quantization And LBG Algorithm Prof. Ch.Srinivasa Kumar Prof. and Head of department. Electronics and communication Nalanda Institute
More informationhave to be modeled) or isolated words. Output of the system is a grapheme-tophoneme conversion system which takes as its input the spelling of words,
A Language-Independent, Data-Oriented Architecture for Grapheme-to-Phoneme Conversion Walter Daelemans and Antal van den Bosch Proceedings ESCA-IEEE speech synthesis conference, New York, September 1994
More informationNUMBERS AND OPERATIONS
SAT TIER / MODULE I: M a t h e m a t i c s NUMBERS AND OPERATIONS MODULE ONE COUNTING AND PROBABILITY Before You Begin When preparing for the SAT at this level, it is important to be aware of the big picture
More informationACOUSTIC EVENT DETECTION IN REAL LIFE RECORDINGS
ACOUSTIC EVENT DETECTION IN REAL LIFE RECORDINGS Annamaria Mesaros 1, Toni Heittola 1, Antti Eronen 2, Tuomas Virtanen 1 1 Department of Signal Processing Tampere University of Technology Korkeakoulunkatu
More information1 st Quarter (September, October, November) August/September Strand Topic Standard Notes Reading for Literature
1 st Grade Curriculum Map Common Core Standards Language Arts 2013 2014 1 st Quarter (September, October, November) August/September Strand Topic Standard Notes Reading for Literature Key Ideas and Details
More informationOPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS
OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,
More informationOCR for Arabic using SIFT Descriptors With Online Failure Prediction
OCR for Arabic using SIFT Descriptors With Online Failure Prediction Andrey Stolyarenko, Nachum Dershowitz The Blavatnik School of Computer Science Tel Aviv University Tel Aviv, Israel Email: stloyare@tau.ac.il,
More informationNCU IISR English-Korean and English-Chinese Named Entity Transliteration Using Different Grapheme Segmentation Approaches
NCU IISR English-Korean and English-Chinese Named Entity Transliteration Using Different Grapheme Segmentation Approaches Yu-Chun Wang Chun-Kai Wu Richard Tzong-Han Tsai Department of Computer Science
More informationarxiv: v1 [cs.cv] 10 May 2017
Inferring and Executing Programs for Visual Reasoning Justin Johnson 1 Bharath Hariharan 2 Laurens van der Maaten 2 Judy Hoffman 1 Li Fei-Fei 1 C. Lawrence Zitnick 2 Ross Girshick 2 1 Stanford University
More informationPhonetic- and Speaker-Discriminant Features for Speaker Recognition. Research Project
Phonetic- and Speaker-Discriminant Features for Speaker Recognition by Lara Stoll Research Project Submitted to the Department of Electrical Engineering and Computer Sciences, University of California
More informationA study of speaker adaptation for DNN-based speech synthesis
A study of speaker adaptation for DNN-based speech synthesis Zhizheng Wu, Pawel Swietojanski, Christophe Veaux, Steve Renals, Simon King The Centre for Speech Technology Research (CSTR) University of Edinburgh,
More informationSemi-supervised methods of text processing, and an application to medical concept extraction. Yacine Jernite Text-as-Data series September 17.
Semi-supervised methods of text processing, and an application to medical concept extraction Yacine Jernite Text-as-Data series September 17. 2015 What do we want from text? 1. Extract information 2. Link
More informationLarge vocabulary off-line handwriting recognition: A survey
Pattern Anal Applic (2003) 6: 97 121 DOI 10.1007/s10044-002-0169-3 ORIGINAL ARTICLE A. L. Koerich, R. Sabourin, C. Y. Suen Large vocabulary off-line handwriting recognition: A survey Received: 24/09/01
More informationEli Yamamoto, Satoshi Nakamura, Kiyohiro Shikano. Graduate School of Information Science, Nara Institute of Science & Technology
ISCA Archive SUBJECTIVE EVALUATION FOR HMM-BASED SPEECH-TO-LIP MOVEMENT SYNTHESIS Eli Yamamoto, Satoshi Nakamura, Kiyohiro Shikano Graduate School of Information Science, Nara Institute of Science & Technology
More informationConversation Starters: Using Spatial Context to Initiate Dialogue in First Person Perspective Games
Conversation Starters: Using Spatial Context to Initiate Dialogue in First Person Perspective Games David B. Christian, Mark O. Riedl and R. Michael Young Liquid Narrative Group Computer Science Department
More informationEvolution of Symbolisation in Chimpanzees and Neural Nets
Evolution of Symbolisation in Chimpanzees and Neural Nets Angelo Cangelosi Centre for Neural and Adaptive Systems University of Plymouth (UK) a.cangelosi@plymouth.ac.uk Introduction Animal communication
More informationMULTILINGUAL INFORMATION ACCESS IN DIGITAL LIBRARY
MULTILINGUAL INFORMATION ACCESS IN DIGITAL LIBRARY Chen, Hsin-Hsi Department of Computer Science and Information Engineering National Taiwan University Taipei, Taiwan E-mail: hh_chen@csie.ntu.edu.tw Abstract
More informationAutomatic Pronunciation Checker
Institut für Technische Informatik und Kommunikationsnetze Eidgenössische Technische Hochschule Zürich Swiss Federal Institute of Technology Zurich Ecole polytechnique fédérale de Zurich Politecnico federale
More informationUNIDIRECTIONAL LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK WITH RECURRENT OUTPUT LAYER FOR LOW-LATENCY SPEECH SYNTHESIS. Heiga Zen, Haşim Sak
UNIDIRECTIONAL LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK WITH RECURRENT OUTPUT LAYER FOR LOW-LATENCY SPEECH SYNTHESIS Heiga Zen, Haşim Sak Google fheigazen,hasimg@google.com ABSTRACT Long short-term
More informationSpeaker Identification by Comparison of Smart Methods. Abstract
Journal of mathematics and computer science 10 (2014), 61-71 Speaker Identification by Comparison of Smart Methods Ali Mahdavi Meimand Amin Asadi Majid Mohamadi Department of Electrical Department of Computer
More informationA Reinforcement Learning Variant for Control Scheduling
A Reinforcement Learning Variant for Control Scheduling Aloke Guha Honeywell Sensor and System Development Center 3660 Technology Drive Minneapolis MN 55417 Abstract We present an algorithm based on reinforcement
More informationUnvoiced Landmark Detection for Segment-based Mandarin Continuous Speech Recognition
Unvoiced Landmark Detection for Segment-based Mandarin Continuous Speech Recognition Hua Zhang, Yun Tang, Wenju Liu and Bo Xu National Laboratory of Pattern Recognition Institute of Automation, Chinese
More informationAnalysis of Emotion Recognition System through Speech Signal Using KNN & GMM Classifier
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735.Volume 10, Issue 2, Ver.1 (Mar - Apr.2015), PP 55-61 www.iosrjournals.org Analysis of Emotion
More informationInternational Journal of Computational Intelligence and Informatics, Vol. 1 : No. 4, January - March 2012
Text-independent Mono and Cross-lingual Speaker Identification with the Constraint of Limited Data Nagaraja B G and H S Jayanna Department of Information Science and Engineering Siddaganga Institute of
More informationLanguage Acquisition Fall 2010/Winter Lexical Categories. Afra Alishahi, Heiner Drenhaus
Language Acquisition Fall 2010/Winter 2011 Lexical Categories Afra Alishahi, Heiner Drenhaus Computational Linguistics and Phonetics Saarland University Children s Sensitivity to Lexical Categories Look,
More informationA Domain Ontology Development Environment Using a MRD and Text Corpus
A Domain Ontology Development Environment Using a MRD and Text Corpus Naomi Nakaya 1 and Masaki Kurematsu 2 and Takahira Yamaguchi 1 1 Faculty of Information, Shizuoka University 3-5-1 Johoku Hamamatsu
More informationPREDICTING SPEECH RECOGNITION CONFIDENCE USING DEEP LEARNING WITH WORD IDENTITY AND SCORE FEATURES
PREDICTING SPEECH RECOGNITION CONFIDENCE USING DEEP LEARNING WITH WORD IDENTITY AND SCORE FEATURES Po-Sen Huang, Kshitiz Kumar, Chaojun Liu, Yifan Gong, Li Deng Department of Electrical and Computer Engineering,
More informationCase study Norway case 1
Case study Norway case 1 School : B (primary school) Theme: Science microorganisms Dates of lessons: March 26-27 th 2015 Age of students: 10-11 (grade 5) Data sources: Pre- and post-interview with 1 teacher
More informationModeling Dialogue Building Highly Responsive Conversational Agents
Modeling Dialogue Building Highly Responsive Conversational Agents ESSLLI 2016 David Schlangen, Stefan Kopp with Sören Klett CITEC // Bielefeld University Who we are Stefan Kopp, Professor for Computer
More informationIntra-talker Variation: Audience Design Factors Affecting Lexical Selections
Tyler Perrachione LING 451-0 Proseminar in Sound Structure Prof. A. Bradlow 17 March 2006 Intra-talker Variation: Audience Design Factors Affecting Lexical Selections Abstract Although the acoustic and
More informationSTUDIES WITH FABRICATED SWITCHBOARD DATA: EXPLORING SOURCES OF MODEL-DATA MISMATCH
STUDIES WITH FABRICATED SWITCHBOARD DATA: EXPLORING SOURCES OF MODEL-DATA MISMATCH Don McAllaster, Larry Gillick, Francesco Scattone, Mike Newman Dragon Systems, Inc. 320 Nevada Street Newton, MA 02160
More informationLecture 10: Reinforcement Learning
Lecture 1: Reinforcement Learning Cognitive Systems II - Machine Learning SS 25 Part III: Learning Programs and Strategies Q Learning, Dynamic Programming Lecture 1: Reinforcement Learning p. Motivation
More informationListening and Speaking Skills of English Language of Adolescents of Government and Private Schools
Listening and Speaking Skills of English Language of Adolescents of Government and Private Schools Dr. Amardeep Kaur Professor, Babe Ke College of Education, Mudki, Ferozepur, Punjab Abstract The present
More informationSegregation of Unvoiced Speech from Nonspeech Interference
Technical Report OSU-CISRC-8/7-TR63 Department of Computer Science and Engineering The Ohio State University Columbus, OH 4321-1277 FTP site: ftp.cse.ohio-state.edu Login: anonymous Directory: pub/tech-report/27
More informationIn how many ways can one junior and one senior be selected from a group of 8 juniors and 6 seniors?
Counting Principle If one activity can occur in m way and another activity can occur in n ways, then the activities together can occur in mn ways. Permutations arrangements of objects in a specific order
More informationCalibration of Confidence Measures in Speech Recognition
Submitted to IEEE Trans on Audio, Speech, and Language, July 2010 1 Calibration of Confidence Measures in Speech Recognition Dong Yu, Senior Member, IEEE, Jinyu Li, Member, IEEE, Li Deng, Fellow, IEEE
More informationLEARNING A SEMANTIC PARSER FROM SPOKEN UTTERANCES. Judith Gaspers and Philipp Cimiano
LEARNING A SEMANTIC PARSER FROM SPOKEN UTTERANCES Judith Gaspers and Philipp Cimiano Semantic Computing Group, CITEC, Bielefeld University {jgaspers cimiano}@cit-ec.uni-bielefeld.de ABSTRACT Semantic parsers
More informationAbout Advisory Committee
About Advisory Committee Contact us Equipment Services User Fee About Advisory Committee Equipment Services User Fee Contact Robert A. Jenkins Microscopy Facility University of Wyoming Laramie, WY 82071
More informationA NOVEL SCHEME FOR SPEAKER RECOGNITION USING A PHONETICALLY-AWARE DEEP NEURAL NETWORK. Yun Lei Nicolas Scheffer Luciana Ferrer Mitchell McLaren
A NOVEL SCHEME FOR SPEAKER RECOGNITION USING A PHONETICALLY-AWARE DEEP NEURAL NETWORK Yun Lei Nicolas Scheffer Luciana Ferrer Mitchell McLaren Speech Technology and Research Laboratory, SRI International,
More information12- A whirlwind tour of statistics
CyLab HT 05-436 / 05-836 / 08-534 / 08-734 / 19-534 / 19-734 Usable Privacy and Security TP :// C DU February 22, 2016 y & Secu rivac rity P le ratory bo La Lujo Bauer, Nicolas Christin, and Abby Marsh
More informationLinguistics 220 Phonology: distributions and the concept of the phoneme. John Alderete, Simon Fraser University
Linguistics 220 Phonology: distributions and the concept of the phoneme John Alderete, Simon Fraser University Foundations in phonology Outline 1. Intuitions about phonological structure 2. Contrastive
More informationLinking object names and object categories: Words (but not tones) facilitate object categorization in 6- and 12-month-olds
Linking object names and object categories: Words (but not tones) facilitate object categorization in 6- and 12-month-olds Anne L. Fulkerson 1, Sandra R. Waxman 2, and Jennifer M. Seymour 1 1 University
More informationRobust Speech Recognition using DNN-HMM Acoustic Model Combining Noise-aware training with Spectral Subtraction
INTERSPEECH 2015 Robust Speech Recognition using DNN-HMM Acoustic Model Combining Noise-aware training with Spectral Subtraction Akihiro Abe, Kazumasa Yamamoto, Seiichi Nakagawa Department of Computer
More informationPRD Online
1 PRD Online 2011-12 SBC PRD Online What is it? PRD Online, part of CPD Online, will keep track of the PRD process for you, allowing you to concentrate on the quality of the professional dialogue. What
More informationQuickStroke: An Incremental On-line Chinese Handwriting Recognition System
QuickStroke: An Incremental On-line Chinese Handwriting Recognition System Nada P. Matić John C. Platt Λ Tony Wang y Synaptics, Inc. 2381 Bering Drive San Jose, CA 95131, USA Abstract This paper presents
More informationINPE São José dos Campos
INPE-5479 PRE/1778 MONLINEAR ASPECTS OF DATA INTEGRATION FOR LAND COVER CLASSIFICATION IN A NEDRAL NETWORK ENVIRONNENT Maria Suelena S. Barros Valter Rodrigues INPE São José dos Campos 1993 SECRETARIA
More informationBUILDING CONTEXT-DEPENDENT DNN ACOUSTIC MODELS USING KULLBACK-LEIBLER DIVERGENCE-BASED STATE TYING
BUILDING CONTEXT-DEPENDENT DNN ACOUSTIC MODELS USING KULLBACK-LEIBLER DIVERGENCE-BASED STATE TYING Gábor Gosztolya 1, Tamás Grósz 1, László Tóth 1, David Imseng 2 1 MTA-SZTE Research Group on Artificial
More informationStudent Perceptions of Reflective Learning Activities
Student Perceptions of Reflective Learning Activities Rosalind Wynne Electrical and Computer Engineering Department Villanova University, PA rosalind.wynne@villanova.edu Abstract It is widely accepted
More informationSaliency in Human-Computer Interaction *
From: AAA Technical Report FS-96-05. Compilation copyright 1996, AAA (www.aaai.org). All rights reserved. Saliency in Human-Computer nteraction * Polly K. Pook MT A Lab 545 Technology Square Cambridge,
More informationRole of Pausing in Text-to-Speech Synthesis for Simultaneous Interpretation
Role of Pausing in Text-to-Speech Synthesis for Simultaneous Interpretation Vivek Kumar Rangarajan Sridhar, John Chen, Srinivas Bangalore, Alistair Conkie AT&T abs - Research 180 Park Avenue, Florham Park,
More informationCorrective Feedback and Persistent Learning for Information Extraction
Corrective Feedback and Persistent Learning for Information Extraction Aron Culotta a, Trausti Kristjansson b, Andrew McCallum a, Paul Viola c a Dept. of Computer Science, University of Massachusetts,
More informationMandarin Lexical Tone Recognition: The Gating Paradigm
Kansas Working Papers in Linguistics, Vol. 0 (008), p. 8 Abstract Mandarin Lexical Tone Recognition: The Gating Paradigm Yuwen Lai and Jie Zhang University of Kansas Research on spoken word recognition
More informationSystem Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks
System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks 1 Tzu-Hsuan Yang, 2 Tzu-Hsuan Tseng, and 3 Chia-Ping Chen Department of Computer Science and Engineering
More informationThe NICT/ATR speech synthesis system for the Blizzard Challenge 2008
The NICT/ATR speech synthesis system for the Blizzard Challenge 2008 Ranniery Maia 1,2, Jinfu Ni 1,2, Shinsuke Sakai 1,2, Tomoki Toda 1,3, Keiichi Tokuda 1,4 Tohru Shimizu 1,2, Satoshi Nakamura 1,2 1 National
More informationEliciting Language in the Classroom. Presented by: Dionne Ramey, SBCUSD SLP Amanda Drake, SBCUSD Special Ed. Program Specialist
Eliciting Language in the Classroom Presented by: Dionne Ramey, SBCUSD SLP Amanda Drake, SBCUSD Special Ed. Program Specialist Classroom Language: What we anticipate Students are expected to arrive with
More informationDegeneracy results in canalisation of language structure: A computational model of word learning
Degeneracy results in canalisation of language structure: A computational model of word learning Padraic Monaghan (p.monaghan@lancaster.ac.uk) Department of Psychology, Lancaster University Lancaster LA1
More informationMultimedia Application Effective Support of Education
Multimedia Application Effective Support of Education Eva Milková Faculty of Science, University od Hradec Králové, Hradec Králové, Czech Republic eva.mikova@uhk.cz Abstract Multimedia applications have
More informationInformatics 2A: Language Complexity and the. Inf2A: Chomsky Hierarchy
Informatics 2A: Language Complexity and the Chomsky Hierarchy September 28, 2010 Starter 1 Is there a finite state machine that recognises all those strings s from the alphabet {a, b} where the difference
More informationSegmental Conditional Random Fields with Deep Neural Networks as Acoustic Models for First-Pass Word Recognition
Segmental Conditional Random Fields with Deep Neural Networks as Acoustic Models for First-Pass Word Recognition Yanzhang He, Eric Fosler-Lussier Department of Computer Science and Engineering The hio
More informationA Computer Vision Integration Model for a Multi-modal Cognitive System
A Computer Vision Integration Model for a Multi-modal Cognitive System Alen Vrečko, Danijel Skočaj, Nick Hawes and Aleš Leonardis Abstract We present a general method for integrating visual components
More informationBAUM-WELCH TRAINING FOR SEGMENT-BASED SPEECH RECOGNITION. Han Shu, I. Lee Hetherington, and James Glass
BAUM-WELCH TRAINING FOR SEGMENT-BASED SPEECH RECOGNITION Han Shu, I. Lee Hetherington, and James Glass Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge,
More informationSurprise-Based Learning for Autonomous Systems
Surprise-Based Learning for Autonomous Systems Nadeesha Ranasinghe and Wei-Min Shen ABSTRACT Dealing with unexpected situations is a key challenge faced by autonomous robots. This paper describes a promising
More informationAirplane Rescue: Social Studies. LEGO, the LEGO logo, and WEDO are trademarks of the LEGO Group The LEGO Group.
Airplane Rescue: Social Studies LEGO, the LEGO logo, and WEDO are trademarks of the LEGO Group. 2010 The LEGO Group. Lesson Overview The students will discuss ways that people use land and their physical
More informationLing/Span/Fren/Ger/Educ 466: SECOND LANGUAGE ACQUISITION. Spring 2011 (Tuesdays 4-6:30; Psychology 251)
Ling/Span/Fren/Ger/Educ 466: SECOND LANGUAGE ACQUISITION Spring 2011 (Tuesdays 4-6:30; Psychology 251) Instructor Professor Joe Barcroft Department of Romance Languages and Literatures Office: Ridgley
More informationSelf-Supervised Acquisition of Vowels in American English
Self-Supervised Acquisition of Vowels in American English Michael H. Coen MIT Computer Science and Artificial Intelligence Laboratory 32 Vassar Street Cambridge, MA 2139 mhcoen@csail.mit.edu Abstract This
More information