TY - CHAP A1 - Perez-Toro, P. A. A1 - Vasquez-Correa, J. C. A1 - Arias-Vergara, T. A1 - Klumpp, P. A1 - Sierra-Castrillon, M. A1 - Roldan-Lopez, M. E. A1 - Aguillon, D. A1 - Hincapie-Henao, L. A1 - Tobon-Quintero, C. A. A1 - Bocklet, Tobias A1 - Schuster, M. A1 - Orozco-Arroyave, J. R. A1 - Nöth, E. T1 - Acoustic and Linguistic Analyses to Assess Early-Onset and Genetic Alzheimer’s Disease T2 - ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) N2 - The PSEN1-E280A or Paisa mutation is responsible for most of Early-Onset Alzheimer’s (EOA) disease cases in Colombia. It affects a large kindred of over 5000 members that present the same phenotype. The most common symptoms are related to language disorders, where speech fluency is also affected due to the difficulty to access semantic information intentionally. This study proposes the use of acoustic and linguistic methods to extract features from speech recordings and their transcriptions to discriminate people with conditions related to the Paisa mutation. We consider state-of-the-art word-embedding methods like Word2Vec and Bidirectional Encoder Representations from Transformer to process the transcripts. The speech signals are modeled by using traditional acoustic features and speaker embeddings. To the best of our knowledge, this is the first study focused on evaluating genetic Alzheimer’s and EOA using acoustics and linguistics. KW - PSEN1–E280A KW - Alzheimer’s Disease KW - Acoustic Analysis KW - Linguistic Analysis Y1 - 2021 SN - 978-1-7281-7605-5 U6 - https://doi.org/10.1109/ICASSP39728.2021.9414009 SP - 8338 EP - 8342 PB - IEEE ER - TY - CHAP A1 - Klumpp, P. A1 - Bocklet, Tobias A1 - Arias-Vergara, T. A1 - Vásquez-Correa, J. C. A1 - Pérez-Toro, P.A. A1 - Bayerl, Sebastian P. A1 - Orozco-Arroyave, J. R. A1 - Nöth, Elmar T1 - The Phonetic Footprint of Covid-19? T2 - Interspeech 2021 N2 - Against the background of the ongoing pandemic, this year’s Computational Paralinguistics Challenge featured a classification problem to detect Covid-19 from speech recordings. The presented approach is based on a phonetic analysis of speech samples, thus it enabled us not only to discriminate between Covid and non-Covid samples, but also to better understand how the condition influenced an individual’s speech signal. Our deep acoustic model was trained with datasets collected exclusively from healthy speakers. It served as a tool for segmentation and feature extraction on the samples from the challenge dataset. Distinct patterns were found in the embeddings of phonetic classes that have their place of articulation deep inside the vocal tract. We observed profound differences in classification results for development and test splits, similar to the baseline method. We concluded that, based on our phonetic findings, it was safe to assume that our classifier was able to reliably detect a pathological condition located in the respiratory tract. However, we found no evidence to claim that the system was able to discriminate between Covid-19 and other respiratory diseases. KW - COVID-19 Y1 - 2021 U6 - https://doi.org/10.21437/Interspeech.2021-1488 SN - 2958-1796 SP - 441 EP - 445 PB - ISCA CY - ISCA ER - TY - CHAP A1 - Vasquez-Correa, J. C. A1 - Bocklet, Tobias A1 - Orozco-Arroyave, J. R. A1 - Nöth, E. T1 - Comparison of User Models Based on GMM-UBM and I-Vectors for Speech, Handwriting, and Gait Assessment of Parkinson’s Disease Patients T2 - ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) N2 - Parkinson's disease is a neurodegenerative disorder characterized by the presence of different motor impairments. Information from speech, handwriting, and gait signals have been considered to evaluate the neurological state of the patients. On the other hand, user models based on Gaussian mixture models - universal background models (GMMUBM) and i-vectors are considered the state-of-the-art in biometric applications like speaker verification because they are able to model specific speaker traits. This study introduces the use of GMM-UBM and i-vectors to evaluate the neurological state of Parkinson's patients using information from speech, handwriting, and gait. The results show the importance of different feature sets from each type of signal in the assessment of the neurological state of the patients. KW - Parkinson’s disease KW - GMM-UBM KW - ivectors KW - gait analysis KW - handwriting analysis KW - speech analysis Y1 - 2020 SN - 978-1-5090-6631-5 U6 - https://doi.org/10.1109/icassp40776.2020.9054348 SP - 6544 EP - 6548 PB - IEEE ER -