Bibliographie
BHAT S., HASEGAWA-JOHNSON M. & SPROAT R. (2010). Automatic fluency assessment by signal-level measurement of spontaneous speech. Second Language Studies : Acquisition, Learning, Education and Technology.
CARDINALETTI A. (2018). Test linguistici accessibili per studenti sordi e con DSA. Pari opportunità per l'accesso all'Università, Milano, Franco Angeli.
CERVINI C. (2018). Le parole nascoste. Cloze e C-test nelle prove di italiano. Test linguistici accessibili per studenti sordi e con DSA. Pari opportunità per l'accesso all'Università, Milano, Franco Angeli, p. 216-232.
CHEN J.-Y. & WANG L. (2010). Automatic lexical stress detection for Chinese learners’ of English. 2010 7th International Symposium on Chinese Spoken Language Processing, p. 407–411.
CHEN L.-Y. & JANG J.-S. (2012). Stress detection of english words for a capt system using word-length dependent gmm-based bayesian classifiers.Interdisciplinary Information Sciences,18, p. 65–70.
COULANGE S. (2016). Remédiation phonétique et phonologique en FLE par une approche multimodale chez les apprenants japonophones. Mémoire de master, Université Grenoble Alpes. Sous la direction de Jean-Marc Colletta et Solange Rossato, en master de Sciences du langage parcours de didactique du français langue étrangère.
COULANGE S. (2019). Proximité rythmique entre apprenants et natifs du français – évaluation d’une métrique basée sur le CEFC. Mémoire de master, Université Grenoble Alpes. Sous la direction de Solange Rossato en master de Sciences du langage parcours industries de la langue.
COULANGE S. & SOLANGE R. (2020). Proximité rythmique entre apprenants et natifs du français – Évaluation d’une métrique basée sur le CEFC. In 6e conférence conjointe Journées d’Études sur la Parole (JEP, 31e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition), p. 118–126, Nancy, France.
DESHMUKH O. & VERMA A. (2009). Nucleus-level clustering for word-independent syllable stress classification. Speech Communication, 51, 1224–1233.
FERRER L., BRATT H., RICHEY C., FRANCO H., ABRASH V. & PRECODA K. (2015). Classification of lexical stress using spectral and prosodic features for computer-assisted language learning systems. Speech Communication, 69 (C), 31–45.
FONTAN L., LECOZ M. & DETEY S. (2018). Automatically measuring L2 speech fluency without the need of ASR : A proof-of-concept study with japanese learners of french. In Interspeech 2018, p. 2544–2548.
HARRISON A., LO W., QIAN X. & MENG H. (2009). Implementation of an extended recognition network for mispronunciation detection and diagnosis in computer-assisted pronunciation training. In SLaTE, p. 45–48.
LI C., LIU J. & XIA S. (2007). English sentence stress detection system based on HMM framework. Appl. Math. Comput.,185 (2), p. 759–768.
SHAHIN M. A., EPPS J. & AHMED B. (2016). Automatic classification of lexical stress in English and Arabic languages using deep learning. In INTERSPEECH 2016, p. 175–179.
TEPPERMAN J. & NARAYANAN S. (2005). Automatic syllable stress detection using prosodic features for pronunciation evaluation of language learners. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., volume 1, p. 937–940.
