W. La-première-colonne-indique-le-numéro-des-fichiers, La première ligne indique le numéro des différents clusters trouvés. L'intersection d'une ligne et d'une colonne indique le nombre de segments

G. Adda, S. Stüker, M. Adda-decker, O. Ambouroue, L. Besacier et al., Breaking the Unwritten Language Barrier: The BULB Project, SLTU-2016 5th Workshop on Spoken Language Technologies for Under-resourced languages 09-12, pp.8-14, 2016.
DOI : 10.1016/j.procs.2016.04.023

URL : https://hal.archives-ouvertes.fr/halshs-01428027

A. Akinlabi and B. Connell, The interaction of linguistic theory, linguistic description and linguistic documentation, Critical Issues in the Study of Linguistics, Languages and Literatures in Nigeria: A Festschrift for Conrad Max Benedict Brann, pp.571-589, 2008.

P. Austin, Current issues in language documentation, in: Language Documentation and Description, SOAS, pp.12-33, 2010.

P. Austin, J. Sallabank, L. Grenoble, C. Grinevald, M. Bert et al., The Cambridge Handbook of Endangered Languages, Handbooks in Language and Linguistics, 2011.
DOI : 10.1017/CBO9780511975981

C. Bannard and D. Matthews, Stored Word Sequences in Language Learning, Psychological Science, vol.24, issue.2, 2008.
DOI : 10.1109/TIT.1978.1055934

URL : http://pubman.mpdl.mpg.de/pubman/item/escidoc:1554374/component/escidoc:2056554/Bannard_2008.pdf

S. Bansal, H. Kamper, S. Goldwater, and A. Lopez, Weakly supervised spoken term discovery using cross-lingual side information, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016.
DOI : 10.1109/ICASSP.2017.7953260

S. Bansal, H. Kamper, A. Lopez, and S. Goldwater, Towards speech-to-text translation without speech recognition, Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, 2017.
DOI : 10.18653/v1/E17-2076

A. Bérard, O. Pietquin, L. Besacier, and C. Servan, Listen and Translate: A Proof of Concept for Endto-End Speech-to-Text Translation, 2016.

V. Berment, Méthodes pour informatiser les langues et les groupes de langues « peu dotées » (phdthesis), 2004.

L. Besacier, E. Barnard, A. Karpov, and T. Schultz, Automatic speech recognition for under-resourced languages: A survey, Speech Communication, vol.56, 2014.
DOI : 10.1016/j.specom.2013.07.008

URL : https://hal.archives-ouvertes.fr/hal-00953644

L. Besacier, B. Zhou, and Y. Gao, TOWARDS SPEECH TRANSLATION OF NON WRITTEN LANGUAGES, 2006 IEEE Spoken Language Technology Workshop, pp.222-225, 2006.
DOI : 10.1109/SLT.2006.326795

B. Birch, Ma Iwaidja Dictionary [WWW Document]. URL https, 2013.

S. Bird, F. R. Hanke, O. Adams, and H. Lee, Aikuma: A Mobile App for Collaborative Language Documentation, Proceedings of the 2014 Workshop on the Use of Computational Methods in the Study of Endangered Languages, pp.1-5, 2014.
DOI : 10.3115/v1/W14-2201

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.672.8735

D. Blachon, E. Gauthier, L. Besacier, G. Kouarata, M. Adda-decker et al., Parallel Speech Collection for Under-resourced Language Studies Using the Lig-Aikuma Mobile Device App, Procedia Computer Science, vol.81, pp.61-66, 2016.
DOI : 10.1016/j.procs.2016.04.030

URL : https://hal.archives-ouvertes.fr/hal-01350065

C. Bowern, Linguistic fieldwork: a practical guide, 2008.

G. Chrupa?a, L. Gelderloos, and A. Alishahi, Representations of language in a model of visually grounded speech signal, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2017.
DOI : 10.18653/v1/P17-1057

H. H. Clark and J. E. Fox-tree, Using uh and um in spontaneous speaking, Cognition, vol.84, issue.1, pp.73-111, 2002.
DOI : 10.1016/S0010-0277(02)00017-3

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.5.7958

M. Cohen, Lettre à Antonio Saura, Pensée Midi, pp.23-33, 1997.

S. Cohen, Bayesian Analysis in Natural Language Processing, Synthesis Lectures on Human Language Technologies, vol.9, issue.2, 2016.
DOI : 10.2200/S00719ED1V01Y201605HLT035

M. Constant and A. Dister, Les disfluences dans les mots composés, 2012.

S. Drude, B. Birch, D. Broeder, P. Withers, and P. Wittenburg, Crowd-sourcing and apps in the field of linguistics: Potentials and challenges of the coming technology, 2013.

L. Duong, A. Anasosopoulos, D. Chiang, S. Bird, and T. Cohn, An Attentional Model for Speech Translation Without Transcription, Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016.
DOI : 10.18653/v1/N16-1109

L. Gelderloss and G. Chrupala, From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning, Proc. COLING 2016 26th Int. Conf. Comput. Linguist, 2016.

J. Gippert, N. P. Himmelmann, and U. Mosel, Essentials of language documentation, Trends in linguistics Studies and monographs, 2006.

P. Godard, G. Adda, M. Adda-decker, A. Allauzen, L. Besacier et al., Preliminary Experiments on Unsupervised Word Discovery in Mboshi, Interspeech 2016, pp.3539-35432016, 2016.
DOI : 10.21437/Interspeech.2016-886

URL : https://hal.archives-ouvertes.fr/hal-01350119

S. Goldwater, T. L. Griffiths, and M. Johnson, Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models, J Mach Learn Res, vol.12, pp.2335-2382, 2011.

S. J. Goldwater, Nonparametric Bayesian Models of Lexical Acquisition, 2006.

D. Harwath and J. Glass, Deep multimodal semantic embeddings for speech and images, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), 2015.
DOI : 10.1109/ASRU.2015.7404800

URL : http://arxiv.org/abs/1511.03690

D. Harwath, A. Torralba, and J. Glass, Unsupervised Learning of Spoken Language with Visual Context, in: Advances in Neural Information Processing Systems, pp.1858-1866, 2016.

H. Hermansky, Perceptual linear predictive (PLP) analysis of speech, The Journal of the Acoustical Society of America, vol.87, issue.4, pp.1738-1752, 1990.
DOI : 10.1121/1.399423

A. Jansen, K. Church, and H. Hermansky, Towards spoken term discovery at scale with zero resources, 2010.

A. Jansen and B. V. Durme, Efficient spoken term discovery using randomized algorithms, 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, pp.2011-401, 2011.
DOI : 10.1109/ASRU.2011.6163965

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.232.4390

M. Johnson, Learning rules with Adaptor Grammars, 2009.

M. Johnson, Unsupervised word segmentation for Sesotho using Adaptor Grammars, Proceedings of the Tenth Meeting of ACL Special Interest Group on Computational Morphology and Phonology, SigMorPhon '08, pp.20-27, 2008.
DOI : 10.3115/1626324.1626328

M. Johnson, Using Adaptor Grammars to Identify Synergies in the Unsupervised Acquisition of Linguistic Structure, Proceedings of ACL-08, pp.398-406, 2008.

M. Johnson and S. Goldwater, Improving nonparameteric Bayesian inference, Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics on, NAACL '09, p.675, 2009.
DOI : 10.3115/1620754.1620800

H. Kamper, S. Settle, G. Shakhnarovich, and K. Livescu, Visually Grounded Learning of Keyword Prediction from Untranscribed Speech, Interspeech 2017, 2017.
DOI : 10.21437/Interspeech.2017-502

C. Lee, T. J. O-'donnell, and J. R. Glass, Unsupervised Lexicon Discovery from Acoustic Input, pp.389-403, 2016.

C. Lehmann, Documentation of endangered languages A priority task for linguistics, ASSidUE Arbeitspapiere Semin. Für Sprachwiss. Univ. Erf, 1999.

C. Liu, J. Trmal, M. Wiesner, C. Harman, and S. Khudanpur, Topic Identification for Speech Without ASR, Interspeech 2017, 2017.
DOI : 10.21437/Interspeech.2017-1093

B. Ludusan, G. Synnaeve, and E. Dupoux, Prosodic boundary information helps unsupervised word segmentation, Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.953-963, 2015.
DOI : 10.3115/v1/N15-1096

B. Ludusan, M. Versteegh, A. Jansen, G. Gravier, X. Cao et al., Bridging the gap between speech technology and natural language processing: an evaluation toolbox for term discovery systems, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01026368

V. Lyzinski, G. Sell, and A. Jansen, An evaluation of graph clustering methods for unsupervised term discovery, INTERSPEECH 2015, 16th Annual Conference of the International Speech Communication Association, pp.3209-3213, 2015.

A. Park and J. R. Glass, Towards unsupervised pattern discovery in speech, IEEE Workshop on Automatic Speech Recognition and Understanding, 2005., pp.53-58, 2005.
DOI : 10.1109/ASRU.2005.1566529

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.137.2498

T. Pellegrini, Transcription automatique de langues peu dotées, 2008.

S. T. Piantadosi, Zipf???s word frequency law in natural language: A critical review and future directions, Psychonomic Bulletin & Review, vol.10, issue.1, pp.1112-1130, 2014.
DOI : 10.1177/102986490601000101

A. Polguère, Notions de base en lexicologie. Observatoire de Linguistique Sens-Texte, 2001.

O. Räsänen, G. Doyle, and M. Frank, Unsupervised word discovery from speech using automatic segmentation into syllable-like units, 2015.

D. K. Roy and A. P. Pentland, Learning words from sights and sounds: a computational model, Cognitive Science, vol.55, issue.3, 2002.
DOI : 10.2307/1130007

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.1156

B. Sands, A. L. Miller, and J. Brugman, The Lexicon in Language Attrition: The Case of N|uu, Presented at the 37th Annual Conference on African Linguistics, Cascadilla Proceedings Project, pp.55-65, 2007.

D. Schwarz, Corpus-Based Concatenative Synthesis, IEEE Signal Processing Magazine, vol.24, issue.2, pp.92-104, 2007.
DOI : 10.1109/MSP.2007.323274

URL : https://hal.archives-ouvertes.fr/hal-01161294

C. Signoret, Exploration des mécanismes non conscients de la perception de la parole : approches comportementales et électroencéphalographiques, 2010.

T. Tsunoda, Language Endangerment and Language Revitalization: An Introduction, 2006.
DOI : 10.1515/9783110896589

URL : http://doi.org/10.26530/oapen_626366

M. Versteegh, X. Anguera, A. Jansen, and E. Dupoux, The Zero Resource Speech Challenge 2015: Proposed Approaches and Results, Procedia Computer Science, vol.81, pp.67-72, 2016.
DOI : 10.1016/j.procs.2016.04.031

URL : http://doi.org/10.1016/j.procs.2016.04.031

A. De, D. Produit, and .. Une-grammaire-adaptative, 37 FIGURE 12, 125 FIGURE 27 DIAGRAMME UML DU SCRIPT COCOWAV_API, p.138

. La-méthode-parse-permet-de-parser-les-timecodes, indiquer précisément le début et la fin de chaque mot, syllabe et phonème En effet, ceux-ci ne le spécifient pas explicitement Ainsi nous transformons des timecode en une version plus structurée, où il est explicitement spécifié quels phonèmes composent les syllabes, et quelles syllabes composent les mots. Nous avons également créé une version statique de la méthode afin que les timecodes obtenus sans initialiser d'objet puissent également être parsés. Celleci se nomme s_parse

?. Timecode-d-'entrée:.., SEPRPHOSEPR, p.3182, 1926.

?. Timecodes-de-sortie..-}, 3182, 'syllable': [ {'begin': 1926.3068, 'end': 2179.3182, 'phoneme': [ {'begin': 1926.3068, 'end': 2050.7955, 'value': 'w'}, {'begin': 2050.7955, 'end': 2144.6591, 'value': 'ai'}, {'begin': 2144, pp.2179-3182, 1926.

T. Résultats and . Le, corpus MBOSHI Nous n'avons reproduit ici que les scores TF-IDF pour les fichiers WAV dont au moins un cluster contenait le nom de l'objet figuré dans l'image, ceux-ci sont surlignés en vert. En rouge figurent les clusters que nous avons classés comme étant non pertinents par observation de la matrice, p.96