458
Views
25
CrossRef citations to date
0
Altmetric
Original Articles

Automatic prediction of intelligible speaking rate for individuals with ALS from speech acoustic and articulatory samples

ORCID Icon, , , ORCID Icon, , , , & ORCID Icon show all
Pages 669-679 | Received 30 Apr 2017, Accepted 28 Jul 2018, Published online: 08 Nov 2018

References

  • Allison, K., Yunusova, Y., Campbell, T., Wang, J., Berry, J., & Green, J. (2017). The diagnostic utility of patient-report and speech-language pathologists’ ratings for detecting the early onset of bulbar symptoms due to ALS. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 18, 358–366. DOI:doi: 10.1080/21678421.2017.1303515
  • Beghi, E., Logroscino, G., Chiò, A., Hardiman, O., Mitchell, D., Swingler, R., Traynor, B.J. … & EURALS Consortium. (2006). The epidemiology of ALS and the role of population-based registries. Biochimica Et Biophysica Acta (BBA)-Molecular Basis of Disease, 1762, 1150–1157. doi: 10.1016/j.bbadis.2006.09.008
  • Berry, J.J. (2011). Accuracy of the NDI wave speech research system. Journal of Speech, Language and Hearing Research 54, 1295–1301. doi: 10.1044/1092-4388(2011/10-0226)
  • Beukelman, D., Fager, S., & Nordness, A. (2011). Communication support for people with ALS. Neurology Research International, 2011, 1. doi: 10.1155/2011/714693
  • Bishop, C.M. (1995). Neural networks for pattern recognition. Oxford University Press, Inc., New York, NY, USA.
  • Breiman, L., Friedman, J., Stone, C.J., & Olshen, R.A. (1984). Classification and regression trees. Boca Raton, FL: CRC press.
  • Cedarbaum, J.M., Stambler, N., Malta, E., Fuller, C., Hilt, D., Thurmond, B., Nakanishi, A. … & 1A complete listing of the BDNF Study Group. (1999). The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. Journal of the Neurological Sciences, 169, 13–21. doi: 10.1016/S0022-510X(99)00210-5
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297.
  • Cummins, N., Scherer, S., Krajewski, J., Schnieder, S., Epps, J., & Quatieri, T.F. (2015). A review of depression and suicide risk assessment using speech analysis. Speech Communication, 71, 10–49. doi: 10.1016/j.specom.2015.03.004
  • Drucker, H., Burges, C.J., Kaufman, L., Smola, A., & Vapnik, V. (1997). Support vector regression machines. Advances in Neural Information Processing Systems, 9, 155–161.
  • Eyben, F., Wöllmer, M., & Schuller, B. (2010, October). Opensmile: The munich versatile and fast open-source audio feature extractor. In Proceedings of the 18th ACM international conference on multimedia (pp. 1459–1462). Firenze: ACM.
  • Falcone, M., Yadav, N., Poellabauer, C., & Flynn, P. (2013). Using isolated vowel sounds for classification of mild traumatic brain injury. In Acoustics, speech and signal processing (ICASSP), 2013 IEEE International Conference on (pp. 7577–7581). Vancouver: IEEE.
  • Friedman, J.H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38, 367–378. doi: 10.1016/S0167-9473(01)00065-2
  • Green, J.R., Wang, J., & Wilson, D.L. (2013). SMASH: a tool for articulatory data processing and analysis. Interspeech 1331–1335.
  • Green, J.R., Yunusova, Y., Kuruvilla, M.S., Wang, J., Pattee, G.L., Synhorst, L., … Berry, J.D. (2013). Bulbar and speech motor assessment in ALS: Challenges and future directions. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 14(7–8), 494–500. doi: 10.3109/21678421.2013.817585
  • Hahm, S., & Wang, J. (2015). Parkinson’s condition estimation using speech acoustic and inversely mapped articulatory data. Proceedings of Interspeech (pp. 513–517). International Speech and Communication Association.
  • Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I.H. (2009). The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter, 11, 10–18. doi: 10.1145/1656274.1656278
  • Horwitz-Martin, R.L., Quatieri, T.F., Lammert, A.C., Williamson, J.R., Yunusova, Y., Godoy, E., … Green, J.R. (2016). Relation of automatically extracted formant trajectories with intelligibility loss and speaking rate decline in amyotrophic lateral sclerosis. Proceedings of Interspeech (pp. 1205–1209).
  • Jiao, Y., Berisha, V., Liss, J., Hsu, S.-C., Levy, E., & McAuliffe, M. (2017). Articulation entropy: An unsupervised measure of articulatory precision. IEEE Signal Processing Letters, 24, 485–489. doi: 10.1109/LSP.2016.2633871
  • Kent, R.D., Sufit, R.L., Rosenbek, J.C., Kent, J.F., Weismer, G., Martin, R.E., & Brooks, B.R. (1991). Speech deterioration in amyotrophic lateral sclerosis. A case study. Journal of Speech, Language, and Hearing Research, 34, 1269–1275. doi: 10.1044/jshr.3406.1269
  • Kiernan, M.C., Vucic, S., Cheah, B.C., Turner, M.R., Eisen, A., Hardiman, O., … Zoing, M.C. (2011). Amyotrophic Lateral Sclerosis. The Lancet, 377, 942–955. doi: 10.1016/S0140-6736(10)61156-7
  • Kim, M., Kim, Y., Yoo, J., Wang, J., & Kim, H. (2017). Regularized speaker adaptation of KL-HMM for dysarthric speech recognition. IEEE Transactions on Neural Systems & Rehabilitation Engineering, 25, 1581–1591. doi: 10.1109/TNSRE.2017.2681691
  • Kim, M., Wang, J., & Kim, H. (2016). Dysarthric speech recognition using Kullback-Leibler divergence-based hidden markov model. Proceedings of Interspeech (pp. 2671–2671). Rockville, MD: ASHA.
  • Langmore, S.E., & Lehman, M.E. (1994). Physiologic deficits in the orofacial system underlying dysarthria in amyotrophic lateral sclerosis. Journal of Speech, Language, and Hearing Research, 37(1), 28–37. doi: 10.1044/jshr.3701.28
  • Little, M.A., McSharry, P.E., Hunter, E.J., Spielman, J., & Ramig, L.O. (2009). Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Trans Biomed Eng, 56, 1015–1022. doi: 10.1109/TBME.2008.2005954
  • Mitchell, T. (1997). Machine learning. Portland: McGraw Hill, 414 pages.
  • Quatieri, T.F., & Malyska, N. (2012). Vocal-Source Biomarkers for Depression: A Link to Psychomotor Activity. Proceedings of Interspeech (pp. 1059–1062).
  • Rabiner, L.R. (1990). A tutorial on hidden Markov models and selected applications in speech recognition. In Readings in Speech Recognition, Alex Waibel and Kai-Fu Lee (Eds.). Morgan Kaufmann Publishers Inc.: San Francisco, CA, USA 267–296.
  • Rong, P., Yunusova, Y., Wang, J., & Green, J.R. (2015). Predicting early bulbar decline in amyotrophic lateral sclerosis: A speech subsystem approach. Behavioural Neurology, 2015, 1–11. doi: 10.1155/2015/183027
  • Rong, P., Yunusova, Y., Wang, J., Zinman, L., Pattee, G.L., Berry, J.D., … Green, J.R. (2016). Predicting speech intelligibility decline in amyotrophic lateral sclerosis based on the deterioration of individual speech subsystems. PLoS One, 11, e0154971, e0154971–e0154919. doi: 10.1371/journal.pone.0154971
  • Sapir, S., Ramig, L.O., Spielman, J.L., & Fox, C. (2010). Formant centralization ratio: a proposal for a new acoustic measure of dysarthric speech. Journal of Speech, Language, and Hearing Research, 53, 114–125. doi: 10.1044/1092-4388(2009/08-0184)
  • Schölkopf, B., Smola, A.J., Williamson, R.C., & Bartlett, P.L. (2000). New support vector algorithms. Neural Computation, 12, 1207–1245. doi: 10.1162/089976600300015565
  • Schuller, B.W., Steidl, S., Batliner, A., Hantke, S., Hönig, F., Orozco-Arroyave, J.R., … Weninger, F. (2015). The INTERSPEECH 2015 computational paralinguistics challenge: nativeness, Parkinson’s & eating condition. Proceedings of Interspeech (pp. 478–482). Dresden: ISCA.
  • Skodda, S., Grönheit, W., & Schlegel, U. (2011). Intonation and speech rate in Parkinson’s disease: General and dynamic aspects and responsiveness to levodopa admission. Journal of Voice, 25, e199–e205. doi: 10.1016/j.jvoice.2010.04.007
  • Skodda, S., Rinsche, H., & Schlegel, U. (2009). Progression of dysprosody in Parkinson’s disease over time-a longitudinal study. Movement Disorders, 24, 716–722. doi: 10.1002/mds.22430
  • Smola, A.J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14, 199–222. doi: 10.1023/B:STCO.0000035301.49549.88
  • Strong, M., & Rosenfeld, J. (2003). Amyotrophic lateral sclerosis: a review of current concepts. Amyotrophic Lateral Sclerosis and Other Motor Neuron Disorders, 4, 136–143. doi: 10.1080/14660820310011250
  • Tomik, J., Tomik, B., Wiatr, M., Składzień, J., Stręk, P., & Szczudlik, A. (2015). The evaluation of abnormal voice qualities in patients with amyotrophic lateral sclerosis. Neurodegenerative Diseases, 15, 225–232. doi: 10.1159/000381956
  • Tsanas, A., Little, M.A., McSharry, P.E., & Ramig, L.O. (2011). Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity. Journal of the Royal Society Interface, 8, 842–855. doi: 10.1098/rsif.2010.0456
  • Vásquez Correa, J.C., Orozco Arroyave, J.R., Arias-Londoño, J.D., Vargas Bonilla, J.F., & Noth, E. (2014). New computer aided device for real time analysis of speech of people with Parkinson’s disease. Revista Facultad De Ingeniería Universidad De Antioquia, 72, 87–103.
  • Wang, J., Green, J.R., & Samal, A. (2013). Individual articulator’s contribution to phoneme production. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on (pp. 7785–7789). Vancouver: IEEE.
  • Wang, J., Green, J.R., Samal, A., & Yunusova, Y. (2013). Articulatory distinctiveness of vowels and consonants: A data-driven approach. Journal of Speech, Language, and Hearing Research, 56, 1539–1551, ASHA: Rockville, MD. doi: 10.1044/1092-4388(2013/12-0030)
  • Wang, J., Hahm, S., & Mau, T. (2015, September). Determining an optimal set of flesh points on tongue, lips, and jaw for continuous silent speech recognition. Proceedings of the 6th Workshop on Speech and Language Processing for Assistive Technologies (pp. 79–85).
  • Wang, J., Kothalkar, P.V., Cao, B., & Heitzman, D. (2016a). Towards automatic detection of amyotrophic lateral sclerosis from speech acoustic and articulatory samples. Proceedings of Interspeech (pp. 1195–1199). San Francisco: ISCA.
  • Wang, J., Kothalkar, P.V., Kim, M., Yunusova, Y., Campbell, T.F., Heitzman, D., & Green, J.R. (2016b). Predicting intelligible speaking rate in individuals with amyotrophic lateral sclerosis from a small number of speech acoustic and articulatory samples. Proceedings of the ACL/ISCA Workshop on Speech and Language Processing for Assistive Technologies (pp. 91–97). San Francisco.
  • Wang, J., Samal, A., Rong, P., & Green, J.R. (2016). An optimal set of flesh points on tongue and lips for speech-movement classification. Journal of Speech, Language, and Hearing Research, 59, 15–26. doi: 10.1044/2015_JSLHR-S-14-0112
  • Weninger, F., Eyben, F., Schuller, B.W., Mortillaro, M., & Scherer, K.R. (2013). On the acoustics of emotion in audio: what speech, music, and sound have in common. Frontiers in Psychology, 4, 292
  • Westbury, J. (1994). X-ray microbeam speech production database user’s handbook (Unpublished manuscript). Madison: University of Wisconsin–Madison.
  • Yorkston, K.M., & Beukelman, D.R. (1981). Communication efficiency of dysarthric speakers as measured by sentence intelligibility and speaking rate,” Journal of Speech and Hearing Disorders, 46, 296–301.
  • Yorkston, K., Beukelman, D., Hakel, M., & Dorsey, M. (2007). Sentence intelligibility test, speech intelligibility test. Lincoln, Neb, USA: Madonna Rehabilitation Hospital.
  • Yunusova, Y., Green, J.R., Greenwood, L., Wang, J., Pattee, G.L., & Zinman, L. (2012). Tongue movements and their acoustic consequences in amyotrophic lateral sclerosis. Folia Phoniatrica Et Logopaedica, 64, 94–102. doi: 10.1159/000336890

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.