A Multi-Modal Feature Embedding Approach to Diagnose Alzheimer Disease from Spoken Language

Introduction: Alzheimer's disease is a type of dementia in which early diagnosis plays a major rule in the quality of treatment. Among new works in the diagnosis of Alzheimer's disease, there are many of them analyzing the voice stream acoustically, syntactically or both. The mostly used tools to perform these analysis usually include machine learning techniques. Objective: Designing an automatic machine learning based diagnosis system will help in the procedure of early detection. Also, systems, using noninvasive data are preferable. Methods: We used are classification system based on spoken language. We use three (statistical and neural) approaches to classify audio signals from spoken language into two classes of dementia and control. Result: This work designs a multi-modal feature embedding on the spoken language audio signal using three approaches; N-gram, i-vector, and x-vector. The evaluation of the system is done on the cookie picture description task from Pitt Corpus dementia bank with the accuracy of 83:6

[1]  Georg Heigold,et al.  End-to-end text-dependent speaker verification , 2015, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Brian Kingsbury,et al.  New types of deep neural network learning for speech recognition and related applications: an overview , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  P. Scheltens,et al.  Advances in the early detection of Alzheimer's disease , 2004, Nature Reviews Neuroscience.

[4]  Lukás Burget,et al.  Language Recognition in iVectors Space , 2011, INTERSPEECH.

[5]  Donald Silberberg,et al.  National Institutes of Health State-of-the-Science Conference Statement: Preventing Alzheimer Disease* and Cognitive Decline , 2010, Annals of Internal Medicine.

[6]  Kathleen C. Fraser,et al.  Linguistic Features Identify Alzheimer's Disease in Narrative Speech. , 2015, Journal of Alzheimer's disease : JAD.

[7]  Xiaohui Peng,et al.  Deep Learning for Sensor-based Activity Recognition: A Survey , 2017, Pattern Recognit. Lett..

[8]  Jekaterina Novikova,et al.  Isolating effects of age with fair representation learning when assessing dementia , 2018, ArXiv.

[9]  B. Dubois,et al.  Early detection of Alzheimer's disease: new diagnostic criteria , 2009, Dialogues in clinical neuroscience.

[10]  Lukás Burget,et al.  Text-dependent speaker verification based on i-vectors, Neural Networks and Hidden Markov Models , 2017, Comput. Speech Lang..

[11]  Xihong Wu,et al.  Human activity recognition with HMM-DNN model , 2015, 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC).

[12]  Hossein Zeinali,et al.  Online signature verification using i-vector representation , 2017, IET Biom..

[13]  Douglas E. Sturim,et al.  SVM Based Speaker Verification using a GMM Supervector Kernel and NAP Variability Compensation , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[14]  F ChenStanley,et al.  An Empirical Study of Smoothing Techniques for Language Modeling , 1996, ACL.

[15]  Colleen Richey,et al.  Aided diagnosis of dementia type through computer-based analysis of spontaneous speech , 2014, CLPsych@ACL.

[16]  Fakhri Karray,et al.  Survey on speech emotion recognition: Features, classification schemes, and databases , 2011, Pattern Recognit..

[17]  Patrick Kenny,et al.  Front-End Factor Analysis for Speaker Verification , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[18]  R. Maccioni,et al.  Neuroinflammation: implications for the pathogenesis and molecular diagnosis of Alzheimer's disease. , 2008, Archives of medical research.

[19]  Sanjeev Khudanpur,et al.  Parallel training of DNNs with Natural Gradient and Parameter Averaging , 2014 .

[20]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition , 2012 .

[21]  Jekaterina Novikova,et al.  The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech , 2018, ArXiv.

[22]  N. Cercone,et al.  Automatic detection and rating of dementia of Alzheimer type through lexical analysis of spontaneous speech , 2005, IEEE International Conference Mechatronics and Automation, 2005.

[23]  Sanjeev Khudanpur,et al.  X-Vectors: Robust DNN Embeddings for Speaker Recognition , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[24]  Yun Lei,et al.  A novel scheme for speaker recognition using a phonetically-aware deep neural network , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[25]  Patrick Kenny,et al.  Joint Factor Analysis Versus Eigenchannels in Speaker Recognition , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[26]  J. Becker,et al.  The natural history of Alzheimer's disease. Description of study cohort and accuracy of diagnosis. , 1994, Archives of neurology.

[27]  Daniel Povey,et al.  The Kaldi Speech Recognition Toolkit , 2011 .

[28]  T. Robbins,et al.  Early Detection and Differential Diagnosis of Alzheimer’s Disease and Depression with Neuropsychological Tasks , 2001, Dementia and Geriatric Cognitive Disorders.

[29]  Douglas A. Reynolds,et al.  Speaker Verification Using Adapted Gaussian Mixture Models , 2000, Digit. Signal Process..

[30]  Nooritawati Md Tahir,et al.  Parkinson Disease gait classification based on machine learning approach , 2012 .

[31]  Stephen Correia,et al.  Impaired Awareness, Behavior Disturbance, and Caregiver Burden in Alzheimer Disease , 2002, Alzheimer disease and associated disorders.

[32]  Sanjeev Khudanpur,et al.  Deep Neural Network Embeddings for Text-Independent Speaker Verification , 2017, INTERSPEECH.

[33]  Giuseppe Carenini,et al.  Detecting Dementia through Retrospective Analysis of Routine Blog Posts by Bloggers with Dementia , 2017, BioNLP.

[34]  H. Möller,et al.  Kinematic Analysis of Handwriting Movements in Patients with Alzheimer’s Disease, Mild Cognitive Impairment, Depression and Healthy Subjects , 2003, Dementia and Geriatric Cognitive Disorders.

[35]  C. Caltagirone,et al.  Facial emotion recognition deficit in amnestic mild cognitive impairment and Alzheimer disease. , 2008, The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry.

[36]  Alzheimer’s Association.  2018 Alzheimer's disease facts and figures , 2018, Alzheimer's & Dementia.

[37]  Erik McDermott,et al.  Deep neural networks for small footprint text-dependent speaker verification , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[38]  Patrick Kenny,et al.  Eigenvoice modeling with sparse training data , 2005, IEEE Transactions on Speech and Audio Processing.

[39]  R. Sperling,et al.  Noninvasive perfusion MRI in Alzheimer's disease , 1996, Neurology.

[40]  Elmar Nöth,et al.  An N-Gram Based Approach to the Automatic Diagnosis of Alzheimer's Disease from Spoken Language , 2017, INTERSPEECH.

[41]  Sanjeev Khudanpur,et al.  Spoken Language Recognition using X-vectors , 2018, Odyssey.

[42]  S. Fay,et al.  Goal setting and attainment in Alzheimer’s disease patients treated with donepezil , 2002, Journal of neurology, neurosurgery, and psychiatry.

[43]  E. Hogervorst,et al.  Recognition of Facial Expressions of Emotion by Patients with Dementia of the Alzheimer Type , 2004, Dementia and Geriatric Cognitive Disorders.

[44]  Donald T. Stuss,et al.  The Dementias , 1996, Brain and Cognition.

[45]  Robert L. Mercer,et al.  Class-Based n-gram Models of Natural Language , 1992, CL.

[46]  C. Bell,et al.  National Institutes of Health State-of-the-Science Conference Statement: Preventing Alzheimer Disease* and Cognitive Decline , 2010, Annals of Internal Medicine.

[47]  Dan Ciresan,et al.  Multi-Column Deep Neural Networks for offline handwritten Chinese character classification , 2013, 2015 International Joint Conference on Neural Networks (IJCNN).

[48]  Koichi Shinoda,et al.  Detecting Alzheimer's Disease Using Gated Convolutional Neural Network from Audio Data , 2018, INTERSPEECH.