Exploiting Pre-Trained ASR Models for Alzheimer's Disease Recognition Through Spontaneous Speech
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[1] Alexei Baevski,et al. wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations , 2020, NeurIPS.
[2] Björn Schuller,et al. Opensmile: the munich versatile and fast open-source audio feature extractor , 2010, ACM Multimedia.
[3] Björn W. Schuller,et al. The INTERSPEECH 2010 paralinguistic challenge , 2010, INTERSPEECH.
[4] Luciana Ferrer,et al. Alzheimer Disease Recognition Using Speech-Based Embeddings From Pre-Trained Models , 2021, Interspeech.
[5] Julian Hough,et al. Alzheimer's Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs , 2021, Interspeech.
[6] Daniele Falavigna,et al. Phonetic and anthropometric conditioning of MSA-KST cognitive impairment characterization system , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.
[7] Björn W. Schuller,et al. The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing , 2016, IEEE Transactions on Affective Computing.
[8] Wanxiang Che,et al. Pre-Training with Whole Word Masking for Chinese BERT , 2019, ArXiv.
[9] Saturnino Luz,et al. A Method for Analysis of Patient Speech in Dialogue for Dementia Detection , 2018, ArXiv.
[10] Dong Wang,et al. CN-Celeb: A Challenging Chinese Speaker Recognition Dataset , 2019, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[11] Jekaterina Novikova,et al. To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection , 2020, INTERSPEECH.
[12] Ronan Collobert,et al. Unsupervised Cross-lingual Representation Learning for Speech Recognition , 2020, Interspeech.
[13] Qian Zhang,et al. Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Loss , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[14] Francis M. Tyers,et al. Common Voice: A Massively-Multilingual Speech Corpus , 2020, LREC.
[15] Kathleen C. Fraser,et al. Linguistic Features Identify Alzheimer's Disease in Narrative Speech. , 2015, Journal of Alzheimer's disease : JAD.
[16] Lysandre Debut,et al. HuggingFace's Transformers: State-of-the-art Natural Language Processing , 2019, ArXiv.
[17] Veronika Vincze,et al. Speaking in Alzheimer’s Disease, is That an Early Sign? Importance of Changes in Language Abilities in Alzheimer’s Disease , 2015, Front. Aging Neurosci..
[18] Fasih Haider,et al. Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge , 2020, INTERSPEECH.
[19] Najim Dehak,et al. Using State of the Art Speaker Recognition and Natural Language Processing Technologies to Detect Alzheimer's Disease and Assess its Severity , 2020, INTERSPEECH.
[20] Hui Bu,et al. AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale , 2018, ArXiv.
[21] Heidi Christensen,et al. Detecting Signs of Dementia Using Word Vector Representations , 2018, INTERSPEECH.
[22] Hao Zheng,et al. AISHELL-1: An open-source Mandarin speech corpus and a speech recognition baseline , 2017, 2017 20th Conference of the Oriental Chapter of the International Coordinating Committee on Speech Databases and Speech I/O Systems and Assessment (O-COCOSDA).
[23] Saturnino Luz,et al. Detecting cognitive decline using speech only: The ADReSSo Challenge , 2021, medRxiv.
[24] Thomas F. Quatieri,et al. Cognitive impairment prediction in the elderly based on vocal biomarkers , 2015, INTERSPEECH.
[25] Margaret Lech,et al. Automated Screening for Alzheimer's Dementia Through Spontaneous Speech , 2020, INTERSPEECH.
[26] Eduardo Coutinho,et al. The INTERSPEECH 2016 Computational Paralinguistics Challenge: Deception, Sincerity & Native Language , 2016, INTERSPEECH.
[27] Shoukang Hu,et al. Development of the Cuhk Elderly Speech Recognition System for Neurocognitive Disorder Detection Using the Dementiabank Corpus , 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[28] Omer Levy,et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.
[29] Joon Son Chung,et al. VoxCeleb2: Deep Speaker Recognition , 2018, INTERSPEECH.
[30] Man-Wai Mak,et al. A Comparative Study of Acoustic and Linguistic Features Classification for Alzheimer's Disease Detection , 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[31] B. MacWhinney. The Childes Project: Tools for Analyzing Talk, Volume II: the Database , 2000 .
[32] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[33] John T. O'Brien,et al. The midlife cognitive profiles of adults at high risk of late-onset Alzheimer's disease: The PREVENT study , 2017, Alzheimer's & Dementia.
[34] Kenneth Ward Church,et al. Disfluencies and Fine-Tuning Pre-Trained Language Models for Detection of Alzheimer's Disease , 2020, INTERSPEECH.
[35] H. Christensen,et al. Using the Outputs of Different Automatic Speech Recognition Paradigms for Acoustic- and BERT-Based Alzheimer's Dementia Detection Through Spontaneous Speech , 2021, Interspeech.
[36] Sara Moccia,et al. Automatic speech analysis to early detect functional cognitive decline in elderly population , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[37] Kris Demuynck,et al. ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification , 2020, INTERSPEECH.
[38] Fasih Haider,et al. An Assessment of Paralinguistic Acoustic Features for Detection of Alzheimer's Dementia in Spontaneous Speech , 2020, IEEE Journal of Selected Topics in Signal Processing.