Classification of speech arrests and speech impairments during awake craniotomy: a multi-databases analysis

Baevski A, Zhou Y, Mohamed A, Auli M (2020) wav2vec 2.0: a framework for self-supervised learning of speech representations. Adv Neural Inf Process Syst 33:12449–12460

Google Scholar 

Mehrish A, Majumder N, Bharadwaj R, Mihalcea R, Poria S (2023) A review of deep learning techniques for speech processing. Inf Fusion 99:101869

Article  Google Scholar 

Hemmerling D, Wodzinski M, Orozco-Arroyave JR, Sztaho D, Daniol M, Jemiolo P, Wojcik-Pedziwiatr M (2023) Vision transformer for parkinson’s disease classification using multilingual sustained vowel recordings, In: 2023 45th Annu Int Conf of the IEEE Eng in Med and Biol Soc (EMBC), pp. 1–4

Veetil IK, Sowmya V, Orozco-Arroyave JR, Gopalakrishnan EA (2024) Robust language independent voice data driven parkinson’s disease detection. Eng Appl Artif Intell 129:107494

Article  Google Scholar 

Ahn K, Cho M, Kim SW, Lee KE, Song Y, Yoo S, Jeon SY, Kim JL, Yoon DH, Kong H-J (2023) Deep learning of speech data for early detection of alzheimer’s disease in the elderly. Bioeng 10(9):1093

Google Scholar 

Meghanani A, ACS, Ramakrishnan AG (2021) An exploration of log-mel spectrogram and mfcc features for alzheimer’s dementia recognition from spontaneous speech, In: 2021 IEEE Spok. Lang. Technolog. Workshop (SLT), pp 670–677

Jouaiti M, Dautenhahn K (2022) Dysfluency classification in stuttered speech using deep learning for real-time applications, In: ICASSP 2022 - 2022 IEEE Int Conf on Acoust, Speech and Signal Process. (ICASSP), pp 6482–6486

Sheikh SA, Sahidullah M, Hirsch F, Ouni S (2023) Advancing stuttering detection via data augmentation, class-balanced loss and multi-contextual deep learning. IEEE J. of Biomed. and Health Inform. 27(5):2553–2564

Article  Google Scholar 

Bayerl SP, Wagner D, Nöth E, Bocklet T, Riedhammer K (2022) The influence of dataset partitioning on dysfluency detection systems, In:Text, Speech, and Dialogue (Sojka P, Horák A, Kopeček I, Pala K (eds)), (Cham). Springer Int. Publishing, pp 423–436

Sheikh SA, Sahidullah M, Hirsch F, Ouni S (2022) Machine learning for stuttering identification: Review, challenges and future directions. Neurocomput. 514:385–402

Article  Google Scholar 

Chua TH, See AAQ, Ang BT, King NKK (2018) Awake craniotomy for resection of brain metastases: A systematic review. World Neurosurg 120:e1128–e1135

Article  PubMed  Google Scholar 

Hande VH, Gunasekaran H, Hegde S, Shashidhar A, Arimappamagan A (2021) Role of clinical neuropsychologists in Awake-Craniotomy. Neurol India 69:711–716

Article  PubMed  PubMed Central  Google Scholar 

Fukutomi Y, Yoshimitsu K, Tamura M, Masamune K, Muragaki Y (2019) Quantitative evaluation of efficacy of intraoperative examination monitor for awake surgery. World Neurosurg 126:e432–e438

Martín-Monzón I, Rivero Ballagas Y, Arias-Sánchez S (2022) Language mapping: A systematic review of protocols that evaluate linguistic functions in awake surgery. Appl Neuropsychol Adult 29(4):845–854

Article  PubMed  Google Scholar 

Nishimura T, Nagao T, Iseki H, Muragaki Y, Tamura M, Minami S (2014) Classification of patient’s reaction in language assessment during awake craniotomy, In: 2014 IEEE 7th Int. Workshop on Comput. Intell. and Appl. (IWCIA), pp 207–212

Okamoto J, Masamune K, Iseki H, Muragaki Y (2018) Development concepts of a smart cyber operating theater (scot) using orin technology. Biomed Eng Biomed Tech 63(1):31–37

Article  Google Scholar 

Yoshimitsu K, Suzuki T, Muragaki Y, Chernov M, Iseki H (2010) Development of modified intraoperative examination monitor for awake surgery (iemas) system for awake craniotomy during brain tumor resection, In: 2010 Ann Int Conf of the IEEE Eng in Med and Biol, pp 6050–6053

Maoudj I, Garraud C, Panheleux C, Saliou V, Seizeur R, Dardenne G (2023) modular system for the synchronized multimodal data acquisition during awake surgery: towards the emergence of a dedicated clinical database*, In: 45th Annu Int Conf of the IEEE Eng in Med and Biol Soc (EMBC), pp. 1–4

Ravanelli M et al (2024) Open-source conversational ai with speechbrain 1.0. J Mach Learn Res 25(333):1–11

Google Scholar 

Sainburg T, Thielk M, Gentner TQ (2020) Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires. PLoS Comput Biol 16(10):e1008228

Article  CAS  PubMed  PubMed Central  Google Scholar 

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

Google Scholar 

Kayama T (2012) The guidelines for awake craniotomy guidelines committee of the japan awake surg. conf. Neurol medico-chir 52(3):119–141

Article  Google Scholar 

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