Abstract
Speech has the potential to provide a rich bio-marker for health, allowing a non-invasive route to early diagnosis and monitoring of a range of conditions related to human physiology and cognition. With the rise of speech related machine learning applications over the last decade, there has been a growing interest in developing speech based tools that perform non-invasive diagnosis. This talk covers two aspects related to this growing trend. One is the collection of large in-the-wild multimodal datasets in which the speech of the subject is affected by certain medical conditions. Our mining effort has been focused on video blogs (vlogs), and explores audio, video, text and metadata cues, in order to retrieve vlogs that include a single speaker which, at some point, admits that he/she is currently affected by a given disease. The second aspect is patient privacy. In this context, we explore recent developments in cryptography and, in particular in Fully Homomorphic Encryption, to develop an encrypted version of a neural network trained with unencrypted data, in order to produce encrypted predictions of health-related labels. As a proof-of-concept, we have selected two target diseases: Cold and Depression, to show our results and discuss these two aspects.
This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references UID/CEC/50021/2013, and SFRH/BD/103402/2014.
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Notes
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The WSM corpus also includes a subset for Parkinson’s Disease, which we excluded for two reasons: space concerns, and the fact that the corresponding lab dataset is aimed at a regression, and not classification task.
References
Boufounos, P., Rane, S.: Secure binary embeddings for privacy preserving nearest neighbors. In: International Workshop on Information Forensics and Security (WIFS) (2011)
Chabanne, H., de Wargny, A., Milgram, J., Morel, C., et al.: Privacy-preserving classification on deep neural network. IACR Cryptology ePrint Archive 2017, 35 (2017)
Chollet, F., et al.: Keras (2015). https://github.com/keras-team/keras
Correia, J., Raj, B., Trancoso, I., Teixeira, F.: Mining multimodal repositories for speech affecting diseases. In: Interspeech (2018)
Cummins, N., Scherer, S., Krajewski, J., Schnieder, S., Epps, J., Quatieri, T.F.: A review of depression and suicide risk assessment using speech analysis. Speech Commun. 71, 10–49 (2015)
Degottex, G., Kane, J., Drugman, T., Raitio, T., Scherer, S.: COVAREP - a collaborative voice analysis repository for speech technologies. In: ICASSP, pp. 960–964, May 2014. https://doi.org/10.1109/ICASSP.2014.6853739
Dias, M., Abad, A., Trancoso, I.: Exploring hashing and cryptonet based approaches for privacy-preserving speech emotion recognition. In: ICASSP. IEEE (2018)
Dibazar, A.A., Narayanan, S., Berger, T.W.: Feature analysis for automatic detection of pathological speech. In: 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, vol. 1, pp. 182–183. IEEE (2002)
Eyben, F., Scherer, K., Schuller, B., Sundberg, J., et al.: The Geneva minimalistic acoustic parameter set (GeMAPS) for voice research and affective computing. IEEE Trans. Affect. Comput. 7(2), 190–202 (2016)
Fan, J., Vercauteren, F.: Somewhat practical fully homomorphic encryption. IACR Cryptology ePrint Archive 2012, 144 (2012). Informal publication
Geitgey, A.: Facerecog (2017). https://github.com/ageitgey/face_recognition
Gilad-Bachrach, R., Dowlin, N., Laine, K., et al.: CryptoNets: applying neural networks to encrypted data with high throughput and accuracy. In: ICML. JMLR Workshop and Conference Proceedings, vol. 48, pp. 201–210 (2016)
Hesamifard, E., Takabi, H., Ghasemi, M.: CryptoDL: deep neural networks over encrypted data. CoRR abs/1711.05189 (2017)
Lopez-de Ipiña, K., et al.: On automatic diagnosis of Alzheimer’s disease based on spontaneous speech analysis and emotional temperature. Cogn. Comput. 7(1), 44–55 (2015)
López-de Ipiña, K., et al.: On the selection of non-invasive methods based on speech analysis oriented to automatic Alzheimer disease diagnosis. Sensors 13(5), 6730–6745 (2013)
Kroenke, K., Strine, T.W., Spitzer, R.L., Williams, J.B., Berry, J.T., Mokdad, A.H.: The PHQ-8 as a measure of current depression in the general population. J. Affect Disord 114(1–3), 163–173 (2009)
Laine, K., Chen, H., Player, R.: Simple encrypted arithmetic library - SEAL v2.3.0. Technical report, Microsoft, December 2017. https://www.microsoft.com/en-us/research/publication/simple-encrypted-arithmetic-library-v2-3-0/
Orozco-Arroyave, J.R., et al.: Characterization methods for the detection of multiple voice disorders: neurological, functional, and laryngeal diseases. IEEE J. Biomed. Health Inform. 19(6), 1820–1828 (2015)
Pathak, M.A., Raj, B.: Privacy-preserving speaker verification and identification using gaussian mixture models. IEEE Trans. Audio Speech Lang. Process. 21(2), 397–406 (2013). https://doi.org/10.1109/TASL.2012.2215602
Rivest, R.L., Adleman, L., Dertouzos, M.L.: On data banks and privacy homomorphisms. Found. Secure Comput. 169–179 (1978)
Rouvier, M., Dupuy, G., Gay, P., Khoury, E., Merlin, T., Meignier, S.: An open-source state-of-the-art toolbox for broadcast news diarization. In: Interspeech (2013)
Schuller, B., et al.: The Interspeech 2017 computational paralinguistics challenge: addressee, cold & snoring. In: Interspeech (2017)
Socher, R., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: EMNLP 2013, pp. 1631–1642 (2013)
Teixeira, F., Abad, A., Trancoso, I.: Patient privacy in paralinguistic tasks. In: Interspeech (2018)
Valstar, M.F., et al.: AVEC 2016 - depression, mood, and emotion recognition workshop and challenge. CoRR abs/1605.01600 (2016). http://arxiv.org/abs/1605.01600
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Trancoso, I., Correia, J., Teixeira, F., Raj, B., Abad, A. (2018). Speech Analytics for Medical Applications. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2018. Lecture Notes in Computer Science(), vol 11107. Springer, Cham. https://doi.org/10.1007/978-3-030-00794-2_3
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