References

Bibliographical references resulting directly from the implementation of the project

Al-Hameed S, Benaissa M, Christensen H (2016). Simple and robust audio-based detection of biomarkers for
Alzheimer’s disease. In Workshop on Speech and Language Processing for Assistive Technologies (SLPAT).
Alghowinem S, Goecke R, Cohn J. F, Wagner M, Parker G, Breakspear M (2015). Cross-cultural detection of
depression from nonverbal behaviour. In Proc. Intl. Conf. on automatic face and gesture recognition (FG).
Amin-Nejad A, Ive J, Velupillai S (2020). Exploring transformer text generation for medical dataset augmen-
tation. In Proceedings of the Twelfth Language Resources and Evaluation Conference.
Aubanel V, Cooke M, Villegas J, Lecumberri M. L. G (2011). Conversing in the presence of a competing
conversation: Effects on speech production. In Proc. Interspeech.
Bachman P (2016). An architecture for deep, hierarchical generative models. In Advances in Neural Information
Processing Systems.
Becker J. T, Boller F, Lopez O. L, Saxton J, McGonigle K. L (1994). The natural history of Alzheimer’s
disease: description of study cohort and accuracy of diagnosis. Archives of Neurology, 51(6):585–594.
Chakraborty R, Pandharipande M, Bhat C, Kopparapu S. K (2020). Identification of Dementia using audio
biomarkers. arXiv preprint arXiv:2002.12788.
Conneau A, Baevski A, Collobert R, Mohamed A, Auli M (2021). Unsupervised Cross-Lingual Representation
Learning for Speech Recognition. In Proc. Interspeech 2021.
Conneau A, Khandelwal K, Goyal N, Chaudhary V, Wenzek G, Guzmán F, Grave E, Ott M, Zettlemoyer L,
Stoyanov V (2020). Unsupervised cross-lingual representation learning at scale. In Proc. ACL.
Davis B, Maclagan M (2010). Pauses, fillers, placeholders and formulaicity in Alzheimer’s discourse. Fillers,
pauses and placeholders, 93:189.
de la Fuente Garcia S, Ritchie C. W, Luz S (2020). Artificial intelligence, speech, and language process-
ing approaches to monitoring Alzheimer’s disease: A systematic review. Journal of Alzheimer’s Disease,
78(4):1547–1574.
Elsey C, Drew P, Jones D, Blackburn D, Wakefield S, Harkness K, Venneri A, Reuber M (2015). Towards
diagnostic conversational profiles of patients presenting with Dementia or functional memory disorders to
memory clinics. Patient Education and Counseling, 98(9):1071–1077.
Eyben F, Scherer K. R, Schuller B. W et al. (2016). The Geneva minimalistic acoustic parameter set (GeMAPS)
for voice research and affective computing. IEEE Transactions on Affective Computing, 7(2):190–202.
Folstein M. F, Folstein S. E, McHugh P. R (1975). Mini-Mental Status. a practical method for grading the
cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3):189–198.
Forbes-McKay K. E, Venneri A (2005). Detecting subtle spontaneous language decline in early Alzheimer’s
disease with a picture description task. Neurological Sciences, 26(4):243–254.
Fraser K. C, Meltzer J. A, Rudzicz F (2016). Linguistic features identify Alzheimer’s disease in narrative
speech. Journal of Alzheimer’s Disease, 49(2):407–422.
Freedman M, Leach L, Kaplan E, Shulman K, Delis D (1994). Clock drawing: A neuropsychological analysis.
Oxford University Press.
Gadzicki K, Khamsehashari R, Zetzsche C (2020). Early vs late fusion in multimodal convolutional neural
networks. In 2020 IEEE 23rd International Conference on Information Fusion (FUSION).
Gkoumas D, Purver M, Liakata M (2023). Reformulating NLP tasks to capture longitudinal manifestation of
language disorders in people with dementia. In Proc. EMNLP.
Guerrero L. K (1996). Attachment-style differences in intimacy and involvement: A test of the four-category
model. Communications Monographs, 63(4):269–292.
Gururangan S, Marasović A, Swayamdipta S, Lo K, Beltagy I, Downey D, Smith N. A (2020). Don’t stop
pretraining: Adapt language models to domains and tasks. In Proc. ACL.
Hu J, Ruder S, Siddhant A, Neubig G, Firat O, Johnson M (2020). XTREME: A massively multilingual
multi-task benchmark for evaluating cross-lingual generalisation. In Proc. ICML.
Huang S, Mamidanna S, Jangam S, Zhou Y, Gilpin L. H (2023). Can large language models explain themselves?
a study of llm-generated self-explanations.
Jin L, Oh Y, Kim H, Jung H, Jon H. J, Shin J. E, Kim E. Y (2023). CONSEN: Complementary and simultaneous
ensemble for Alzheimer’s disease detection and MMSE score prediction. In Proc. ICASSP.
Jones D, Drew P, Elsey C, Blackburn D, Wakefield S, Harkness K, Reuber M (2016). Conversational assessment
in memory clinic encounters: interactional profiling for differentiating Dementia from functional memory
disorders. Aging & Mental Health, 20(5):500–509.
Kavé G, Dassa A (2018). Severity of Alzheimer’s disease and language features in picture descriptions.
Aphasiology, 32(1):27–40.
Kemper S, Greiner L. H, Marquis J. G, Prenovost K, Mitzner T. L (2001). Language decline across the life
span: findings from the Nun Study. Psychology and Aging, 16(2):227–239.
Keung P, Lu Y, Bhardwaj V (2019). Adversarial learning with contextual embeddings for zero-resource cross-
lingual classification and NER. In Proc. EMNLP-IJCNLP.
Kokalj E, Škrlj B, Lavrač N, Pollak S, Robnik-Šikonja M (2021). BERT meets Shapley: Extending SHAP
explanations to transformer-based classifiers. In Proc. EACL Hackashop on News Media Content Analysis.
Le X, Lancashire I, Hirst G, Jokel R (2011). Longitudinal detection of dementia through lexical and syntactic
changes in writing: a case study of three British novelists. Literary & Linguistic Computing, 26(4):435–461.
Liang Y, Duan N, Gong Y, Wu N, Guo F et al. (2020). XGLUE: A new benchmark dataset for cross-lingual
pre-training, understanding and generation. In Proc. EMNLP.
Liu N, Wei K, Sun X, Yu H et al. (2022). Assist non-native viewers: Multimodal cross-lingual summarization
for how2 videos. In Proc. EMNLP.
Liu P, Yuan W, Fu J, Jiang Z, Hayashi H, Neubig G (2023). Pre-train, prompt, and predict: A systematic
survey of prompting methods in natural language processing. ACM Comput. Surv., 55(9).
Luz S, Haider F, Fromm D, Lazarou I, Kompatsiaris I, MacWhinney B (2023). Multilingual Alzheimer’s dementia recognition through spontaneous speech: a signal processing grand challenge. arXiv:2301.05562.
Manouilidou C, Roumpea G, Nousia A, Stavrakaki S, Nasios G (2020). Revisiting aspect in mild cognitive
impairment and Alzheimer’s disease: evidence from Greek. Frontiers in Communication, 5(10).
Mei K, Ding X, Liu Y, Guo Z, Xu F, Li X, Naren T, Yuan J, Ling Z (2023). The USTC system for ADReSS-M
challenge. In Proc. ICASSP.
Nasreen S, Hough J, Purver M et al. (2021a). Detecting Alzheimer’s disease using interactional and acoustic
features from spontaneous speech. In Proc. Interspeech.
Nasreen S, Purver M, Hough J (2019). A corpus study on questions, responses and misunderstanding signals
in conversations with Alzheimer’s patients. In Proc. SemDial.
Nasreen S, Rohanian M, Hough J, Purver M (2021b). Alzheimer’s Dementia recognition from spontaneous
speech using disfluency and interactional features. Frontiers in Computer Science, page 49.
Noone P (2015). Addenbrooke’s Cognitive Examination-III. Occupational Medicine, 65:418–420.
Nozza D, Bianchi F, Hovy D (2020). What the [mask]? making sense of language-specific bert models. arXiv
preprint arXiv:2003.02912.
Pelicon A, Shekhar R, Škrlj B, Purver M, Pollak S (2021). Investigating cross-lingual training for offensive language detection. PeerJ Computer Science, 7:e559.
Poncelas A, Shterionov D, Way A et al. (2018). Investigating backtranslation in neural machine translation. In Proc. EAMT.
Pope C, Davis B. H (2011). Finding a balance: The Carolinas Conversation Collection. Corpus Linguistics
and Linguistic Theory, 7(1):143–161.
Pérez Mantero J. L (2014). Interacción y predictibilidad: los intercambios conversacionales con hablantes con
demencia tipo Alzhéimer. Revista de Investigación Lingüística, 17:97–118.
Rohanian M, Hough J, Purver M (2020). Multi-modal fusion with gating using audio, lexical and disfluency
features for Alzheimer’s dementia recognition from spontaneous speech. In Proceedings of INTERSPEECH.
Rohanian M, Hough J, Purver M (2021). Alzheimer’s dementia recognition using acoustic, lexical, disfluency
and speech pause features robust to noisy inputs. In Proceedings of INTERSPEECH.
Ruder S, Constant N, Botha J, Siddhant A, Firat O, Fu J, Liu P, Hu J, Garrette D, Neubig G, Johnson M
(2021). XTREME-R: Towards more challenging and nuanced multilingual evaluation. In Proc. EMNLP.
Rusko M, Sabo R, Trnka M, Zimmermann A, Malaschitz R, Ružický E, Brandoburová P, Kevická V, Škorvánek
M (2023). Ewa-db, slovak database of speech affected by neurodegenerative diseases. medRxiv.
Sarawgi U, Zulfikar W, Soliman N, Maes P (2020). Multimodal inductive transfer learning for detection of
Alzheimer’s dementia and its severity. arXiv preprint arXiv:2009.00700.
Singh C, Askari A, Caruana R, Gao J (2023). Augmenting interpretable models with large language models
during training. Nature Communications, 14:7913.
Singh S, Bucks R. S, Cuerden J. M (2001). Evaluation of an objective technique for analysing temporal
variables in dat spontaneous speech. Aphasiology, 15(6):571–583.
Song Y, Khanuja S, Liu P, Faisal F, Ostapenko A, Winata G, Aji A, Cahyawijaya S, Tsvetkov Y et al. (2023).
GlobalBench: A benchmark for global progress in natural language processing. In Proc. EMNLP.
St-Pierre M.-C, Ska B, Béland R (2005). Lack of coherence in the narrative discourse of patients with dementia
of the Alzheimer’s type. Journal of Multilingual Communication Disorders, 3(3):211–215.
Stivers T, Enfield N. J, Brown P, Englert C, Hayashi M et al. (2009). Universals and cultural variation in
turn-taking in conversation. Proceedings of the National Academy of Sciences, 106(26):10587–10592.
Tamm B, Vandenberghe R, Van Hamme H (2023). Cross-lingual transfer learning for Alzheimer’s detection
from spontaneous speech. In Proceedings of ICASSP.
Templeton E. M, Chang L. J, Reynolds E. A, Cone LeBeaumont M. D, Wheatley T (2022). Fast response
times signal social connection in conversation. Proc. National Academy of Sciences, 119(4):e2116915119.
Varlokosta S, Fragkopoulou K, Arfani D, Manouilidou C (2023). Methodologies for assessing morphosyntactic
ability in people with Alzheimer’s disease. International Journal of Language & Communication Disorders.
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A. N, Kaiser Ł, Polosukhin I (2017). Attention
is all you need. In Advances in Neural Information Processing Systems.
Wei J, Zou K (2019). EDA: Easy data augmentation techniques for boosting performance on text classification
tasks. In Proc. EMNLP-IJCNLP.
Xie H, Cui J, Cao Y, Chen J, Tao J, Fan C et al. (2023). Multimodal cross-lingual features and weight fusion
for cross-cultural humor detection. In Proc. Multimodal Sentiment Analysis Challenge and Workshop.
Yuan J, Bian Y, Cai X, Huang J, Ye Z, Church K (2020). Disfluencies and fine-tuning pre-trained language
models for detection of Alzheimer’s disease. In Proc. Interspeech.
Zhang C, Jepson K, Lohfink G, Arvaniti A (2021). Comparing acoustic analyses of speech data collected
remotely. Journal of the Acoustical Society of America, 149(6):3910–3916.