Toward assessment of human voice biomarkers of brain lesions through explainable deep learning

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

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Praat, a system for doing phonetics by computer

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The impact of traumatic brain injuries: A global perspective

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Traumatic brain injury: progress and challenges in prevention, clinical care, and research

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Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson’s Disease

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Comparison of feature importance measures as explanations for classification models

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Easyalign: an automatic phonetic alignment tool under praat

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Predicting outcome and recovery after stroke with lesions extracted from MRI images

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Blood Biomarkers for Traumatic Brain Injury: A Quantitative Assessment of Diagnostic and Prognostic Accuracy

Gan, Z. S., Stein, S. C., Swanson, R. L., Guan, S., Garcia, L. Y., Mehta, D. M., & Smith, D. H. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00446 · Cited by 171 · see citing works

The role of the right hemisphere in the control of speech prosody in propositional and affective contexts

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Major depressive disorder discrimination using vocal acoustic features

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Multi-Feature Analysis for Automated Brain Stroke Classification Using Weighted Gaussian Naïve Bayes Classifier

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Giving Voice to Vulnerable Children: Machine Learning Analysis of Speech Detects Anxiety and Depression in Early Childhood

McGinnis, E. W., Anderau, S. P., Hruschak, J. L., Gurchiek, R. D., Lopez‐Duran, N. L., Fitzgerald, K. D., Rosenblum, K. L., Muzik, M., & McGinnis, R. S. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2913590 · Cited by 125 · see citing works

Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation

Pereira, S., Meier, R., McKinley, R., Wiest, R., Alves, V., Silva, C. A., & Reyes, M. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.12.009 · Cited by 110 · see citing works

Acoustic differences between healthy and depressed people: a cross-situation study

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Contribution of the Cerebellum and the Basal Ganglia to Language Production: Speech, Word Fluency, and Sentence Construction—Evidence from Pathology

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Interpretation of Depression Detection Models via Feature Selection Methods

Alghowinem, S., Gedeon, T., Goecke, R., Cohn, J. F., & Parker, G. B. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3035535 · Cited by 86 · see citing works

Orbito-frontal lesions cause impairment during late but not early emotional prosodic processing

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Emotional speech in Parkinson's disease

Möbes, J., Joppich, G., Stiebritz, F., Dengler, R., & Schröder, C. (2008) · Movement Disorders · doi:10.1002/mds.21940 · Cited by 70 · see citing works

Feature Extraction and Classification Techniques for Speech Recognition: A Review

Desai, N., Dhameliya, K., & Desai, V. (2013) · Cited by 69 · see citing works

Detection of major depressive disorder using vocal acoustic analysis and machine learning—an exploratory study

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An End-to-End Model for Detection and Assessment of Depression Levels using Speech

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A time-frequency channel attention and vectorization network for automatic depression level prediction

Niu, M., Liu, B., Tao, J., & Li, Q. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.056 · Cited by 48 · see citing works

On the importance of components of the MFCC in speech and speaker recognition

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Cost-Effectiveness of Biomarker Screening for Traumatic Brain Injury

Su, Y. S., Schuster, J. M., Smith, D. H., & Stein, S. C. (2018) · Journal of Neurotrauma · doi:10.1089/neu.2018.6020 · Cited by 36 · see citing works

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients

Sumali, B., Mitsukura, Y., Liang, K., Yoshimura, M., Kitazawa, M., Takamiya, A., Fujita, T., Mimura, M., & Kishimoto, T. (2020) · Sensors · doi:10.3390/s20123599 · Cited by 36 · see citing works

Comparative processing of emotional prosody and semantics following basal ganglia infarcts: ERP evidence of selective impairments for disgust and fear

Paulmann, S., Pell, M. D., & Kotz, S. A. (2009) · Brain Research · doi:10.1016/j.brainres.2009.07.102 · Cited by 35 · see citing works

Detection of replay spoof speech using teager energy feature cues

Kamble, M. R., & Patil, H. A. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101140 · Cited by 32 · see citing works

Human-Machine Interaction Personalization: a Review on Gender and Emotion Recognition Through Speech Analysis

Mura, M. L., & Lamberti, P. (2020) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroind4.0iot48571.2020.9138203 · Cited by 27 · see citing works

Affective prosody in the reading voice of stroke patients.

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Disfluency clusters in speakers with and without neurogenic stuttering following traumatic brain injury

Penttilä, N., Korpijaakko‐Huuhka, A., & Kent, R. D. (2019) · Journal of Fluency Disorders · doi:10.1016/j.jfludis.2019.01.001 · Cited by 18 · see citing works

Constant Q Cepstral coefficients for classification of normal vs. Pathological infant cry

Patil, H. A., Patil, A. T., & Kachhi, A. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746946 · Cited by 18 · see citing works

A machine learning approach for predicting post-stroke aphasia recovery

Gu, Y., Bahrani, M., Billot, A., Lai, S., Braun, E. J., Varkanitsa, M., Bighetto, J., Rapp, B. C., Parrish, T. B., Caplan, D. N., et al. (2020) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3389189.3389204 · Cited by 12 · see citing works

On the orbito-striatal interface in (acoustic) emotional processing

Kotz, S. A., Hasting, A. S., & Paulmann, S. (2013) · doi:10.1093/acprof:oso/9780199583560.003.0014 · Cited by 4 · see citing works

Feature Analysis for Aphasic or Abnormal Language Caused by Injury

Roldán-Palacios, M., & López‐López, A. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-80129-8_1 · Cited by 2 · see citing works

Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review

Ebraheem, M., Toghranegar, J., Bridge2AI-Voice Consortium, Bensoussan, Y., & Templeton, J. M. (2026) · Journal of Medical Internet Research · doi:10.2196/83790

Automatic Utterance Selection Based on Prosodic Features of Children’s Vocalizations

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Identification of utterance intention in Japanese spontaneous spoken dialogue by use of prosody and keyword information

Kurematsu, A., & Shionoya, Y. (2000) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2000-24

Infant cry recognition based on feature extraction

Yuan, F.-L., & Huang, X. (2010) · doi:10.1109/icina.2010.5636971 · Cited by 3 · see citing works

Pitch-based cepstral features for gender classification in noisy environments

Kumar, P., & Chandra, M. (2013) · International Journal of Signal and Imaging Systems Engineering · doi:10.1504/ijsise.2013.054792 · Cited by 1 · see citing works

Application of Biomimetic Technology to Feature Extraction from Acoustic Objects

Yuejua, S. (2014) · Journal of Projectiles.Rockets.Missiles and Guidance