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1
Auditory syllabic identification enhanced by non-informative visible speech
In: http://www.gipsa-lab.grenoble-inp.fr/%7Echristophe.savariaux/PDF/AVSP03_JLS.pdf (2013)
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2
A Simple Hybrid Acoustic / Morphologically-Constrained Technique for the Synthesis of Stop Consonants in Various Vocalic Contexts
In: http://hal.inria.fr/docs/00/80/75/19/PDF/Interspeech_2012_FB_LG_LJB_updated.pdf (2013)
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3
Extraction semi-automatique des mouvements du tractus vocal à partir de données cinéradiographiques
Fontecave, Julie. - : HAL CCSD, 2006
In: https://tel.archives-ouvertes.fr/tel-00203082 ; Traitement du signal et de l'image [eess.SP]. Institut National Polytechnique de Grenoble - INPG, 2006. Français (2006)
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4
DCT-based video features for audio-visual speech recognition
In: http://home.arcor.de/martin.heckmann/Publications/2000-2003/ICSLP02.pdf (2002)
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5
Optimal Weigthing of Posteriors for Audio-Visual Speech Recognition
In: http://www.dcs.shef.ac.uk/research/groups/spandh/projects/respite/publications/heckmann_icassp_01.ps.gz (2001)
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6
Optimal Weigthing of Posteriors for Audio-Visual Speech Recognition
In: http://mti.xidian.edu.cn/multimedia/2001/supp/icassp2001/MAIN/papers/pap1012.pdf (2001)
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7
OPTIMAL WEIGHTING OF POSTERIORS FOR AUDIO-VISUAL SPEECH RECOGNITION
In: http://www.sfb588.uni-karlsruhe.de/TextdateienSFB/./publikationen/2001_04.ps (2001)
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8
Comparing Audio- and A-Posteriori-Probability-Based Stream Confidence Measures for Audio-Visual Speech Recognition
In: http://www.dcs.shef.ac.uk/research/groups/spandh/projects/respite/publications/heckmann_eurospeech_01.ps.gz (2001)
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9
Comparing audio- and aposteriori-probability-based stream confidence measures for audio-visual speech recognition
In: http://home.arcor.de/martin.heckmann/Publications/2000-2003/EUROSPEECH01.pdf (2001)
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10
A CASA-Labelling Model Using The Localisation Cue For Robust Cocktail-Party Speech Recognition
In: http://www.icp.inpg.fr/~tessier/ps/es99.ps.gz (1999)
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11
A Casa Front-End Using The Localisation Cue For Segregation And Then Cocktail-Party Speech Recognition
In: http://www.dcs.shef.ac.uk/research/groups/spandh/projects/respite/publications/tessier_icsp_99.ps.gz (1999)
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12
A Casa-Labelling Model Using The Localisation Cue For Robust Cocktail-Party Speech Recognition
In: http://www.dcs.shef.ac.uk/research/groups/spandh/projects/respite/publications/glotin_eurospeech_99.ps.gz (1999)
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13
Binding and unbinding in the McGurk effect Nahorna et al. 1
In: http://hal.univ-grenoble-alpes.fr/docs/00/96/84/08/PDF/123916_2_merged_1329924183.pdf
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14
ISCA Archive A Phonetically Neutral Model of the Low-level Audiovisual Interaction
In: http://isca-speech.org/archive_open/archive_papers/avsp03/av03_089.pdf
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15
ISCA Archive AUDITORY SYLLABIC IDENTIFICATION ENHANCED BY NON-INFORMATIVE VISIBLE SPEECH
In: http://isca-speech.org/archive_open/archive_papers/avsp03/av03_019.pdf
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16
A semi-automatic method for extracting vocal-tract movements from x-ray films
In: http://hal.archives-ouvertes.fr/docs/00/37/32/63/PDF/Speech_com_fontecave1_s3.pdf
Abstract: Despite the development of new imaging techniques, existing X-ray data remain an appropriate tool to study speech production phenomena. However, to exploit these images, the shapes of the vocal tract articulators must first be extracted. This task, usually manually realized, is long and laborious. This paper describes a semi-automatic technique for facilitating the extraction of vocal tract contours from complete sequences of large existing cineradiographic databases in the context of continuous speech production. The proposed method efficiently combines the human expertise required for marking a small number of key images and an automatic indexing of the video data to infer dynamic 2D data. Manually acquired geometrical data are associated to each image of the sequence via a similarity measure based on the low frequency Discrete Cosine Transform (DCT) components of the images. Moreover to reduce the reconstruction error and improve the geometrical contour estimation, we perform post-processing treatments, such as a neighborhood averaging and a temporal filtering. The method is applied independently for each articulator (tongue, velum, lips, and mandible). Then the acquired contours are combined to reconstruct the movements of the entire vocal tract. We carry out evaluations, including comparisons with manual markings and with another semi-automatic method.
Keyword: Cineradiography; contour extraction; Key words; low frequency DCT components; vocal
URL: http://hal.archives-ouvertes.fr/docs/00/37/32/63/PDF/Speech_com_fontecave1_s3.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.370.5461
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17
Audio-Visual Recognition of Spectrally Reduced Speech
In: http://www.dcs.shef.ac.uk/research/groups/spandh/projects/respite/publications/bertho_avsp_01.pdf
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18
A Multi-Stage Methodology To Setup An ANN/HMM Audio-Visual Speech Recognition System
In: http://www.dcs.shef.ac.uk/research/groups/spandh/projects/respite/publications/heckmann_IAR_00.ps.gz
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19
AVSP 2001 International Conference on Auditory-Visual Speech Processing Audio-visual recognition of spectrally reduced speech
In: http://isca-speech.org/archive_open/archive_papers/avsp01/av01_183.pdf
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