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41
A Visual Context-Aware Multimodal System for Spoken Language Processing
In: http://www.media.mit.edu/cogmac/publications/niloy-euro03.pdf (2003)
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42
Augmenting User Interfaces with Adaptive Speech Commands
In: http://www.media.mit.edu/cogmac/publications/jfig_icmi03.pdf (2003)
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43
Augmenting User Interfaces with Adaptive Speech Commands
In: http://web.media.mit.edu/~pgorniak/jfig.pdf (2003)
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44
Learning Word Meanings and Descriptive Parameter Spaces from Music
In: http://www.media.mit.edu/cogmac/publications/whitman03learning.pdf (2003)
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45
Grounded spoken language acquisition: Experiments in word learning
In: http://web.media.mit.edu/~dkroy/papers/pdf/roy_2003.pdf (2003)
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46
Grounded Spoken Language Acquisition: Experiments in Word Learning
In: http://www.media.mit.edu/cogmac/publications/ieee_multimedia_2003.pdf (2003)
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47
Spontaneous speech recognition using HMMs
Yoder, Benjamin W. (Benjamin Wesley), 1977-. - : Massachusetts Institute of Technology, 2003
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48
Learning Visually Grounded Words and Syntax of Natural Spoken Language
In: http://web.media.mit.edu/~dkroy/papers/pdf/ec.pdf (2002)
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49
Grounded Spoken Language Acquisition: Experiments in Word Learning
In: http://www.media.mit.edu/cogmac/ieee_mm_2002.pdf (2002)
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50
Automatic utterance segmentation in spontaneous speech
Yoshida, Norimasa, 1979-. - : Massachusetts Institute of Technology, 2002
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51
Learning Words from Sights and Sounds: A Computational Model
In: http://web.media.mit.edu/~dkroy/papers/pdf/cogsci_v2.pdf (2001)
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52
Learning visually grounded words and syntax of natural spoken language
In: http://web.media.mit.edu/~dkroy/papers/pdf/roy_2000_2001.pdf (2001)
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53
Integration Of Speech And Vision Using Mutual Information
In: http://vismod.www.media.mit.edu/people/dkroy/papers/pdf/icassp2000.pdf (2000)
Abstract: We are developing a system which learns words from co-occurring spoken and visual input. The goal is to automatically segment continuous speechatword boundaries without a lexicon, and to form visual categories which correspond to spoken words. Mutual information is used to integrate acoustic and visual distance metrics in order to extract an audio-visual lexicon from raw input. Wereport results of experiments with a corpus of infant-directed speech and images. 1. INTRODUCTION We are developing systems which learn words from co-occurring audio and visual input [5, 4]. Input consists of naturally spoken mutliword utterances paired with visual representations of object shapes (Figure 1). Output of the system is an audio-visual lexicon of sound-shape associations which encode acoustic forms of words (or phrases) and their visually grounded referents. We assume that, in general, the audio and visual signals are uncorrelated in time. However, when a wordisspoken, its visual representatio.
URL: http://vismod.www.media.mit.edu/people/dkroy/papers/pdf/icassp2000.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.9060
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54
Integration of speech and vision using mutual information
In: http://www.icsi.berkeley.edu/~dpwe/research/etc/icassp2000/pdf/143_717.PDF (2000)
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55
Learning Words from Sights and Sounds: A Computational Model
In: http://www.media.mit.edu/cogmac/cogsci_2002.pdf (2000)
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56
A Computational Model of Word Learning from Multimodal Sensory Input
In: http://vismod.www.media.mit.edu/~dkroy/papers/pdf/iccm2000.pdf (2000)
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57
Learning Visually Grounded Words and Syntax of Natural Spoken Language
In: http://www.media.mit.edu/cogmac/evol_comm_2002.pdf (2000)
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58
Learning Words From Natural Audio-Visual Input
In: http://vismod.www.media.mit.edu/~dkroy/papers/Postscript/icslp98.ps.Z (1999)
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59
Learning Words From Natural Audio-Visual Input
In: http://vismod.www.media.mit.edu/~dkroy/papers/pdf/icslp98.pdf (1999)
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60
Multimodal Adaptive Interfaces
In: ftp://whitechapel.media.mit.edu/pub/tech-reports/TR-438.ps.Z (1997)
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