No AccessJournal of Speech, Language, and Hearing ResearchResearch Article9 Nov 2017

Predicting Intelligibility Gains in Dysarthria Through Automated Speech Feature Analysis

    Purpose

    Behavioral speech modifications have variable effects on the intelligibility of speakers with dysarthria. In the companion article, a significant relationship was found between measures of speakers' baseline speech and their intelligibility gains following cues to speak louder and reduce rate (Fletcher, McAuliffe, Lansford, Sinex, & Liss, 2017). This study reexamines these features and assesses whether automated acoustic assessments can also be used to predict intelligibility gains.

    Method

    Fifty speakers (7 older individuals and 43 with dysarthria) read a passage in habitual, loud, and slow speaking modes. Automated measurements of long-term average spectra, envelope modulation spectra, and Mel-frequency cepstral coefficients were extracted from short segments of participants' baseline speech. Intelligibility gains were statistically modeled, and the predictive power of the baseline speech measures was assessed using cross-validation.

    Results

    Statistical models could predict the intelligibility gains of speakers they had not been trained on. The automated acoustic features were better able to predict speakers' improvement in the loud condition than the manual measures reported in the companion article.

    Conclusions

    These acoustic analyses present a promising tool for rapidly assessing treatment options. Automated measures of baseline speech patterns may enable more selective inclusion criteria and stronger group outcomes within treatment studies.

    References

    • Bayestehtashk, A., Asgari, M., Shafran, I., & McNames, J. (2015). Fully automated assessment of the severity of Parkinson's disease from speech.Computer Speech & Language, 29(1), 172–185.
    • Berisha, V., Sandoval, S., Utianski, R., Liss, J., & Spanias, A. (2013). Selecting disorder-specific features for speech pathology fingerprinting. Paper presented at the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada.
    • Berisha, V., Utianski, R., & Liss, J. (2013). Towards a clinical tool for automatic intelligibility assessment. Paper presented at the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada.
    • Cannito, M. P., Suiter, D. M., Beverly, D., Chorna, L., Wolf, T., & Pfeiffer, R. M. (2012). Sentence intelligibility before and after voice treatment in speakers with idiopathic Parkinson's disease.Journal of Voice, 26(2), 214–219.
    • Darley, F. L., Aronson, A. E., & Brown, J. R. (1975). Motor speech disorders (Vol. 304). Philadelphia, PA: Saunders.
    • de Boer, G., & Bressmann, T. (2016). Application of linear discriminant analysis to the long-term averaged spectra of simulated disorders of oral–nasal balance.The Cleft Palate-Craniofacial Journal, 53(5), e163–e171.
    • Fletcher, A., & McAuliffe, M. (2017). Examining variation in treatment outcomes among speakers with dysarthria.Seminars in Speech and Language, 38(03), 191–199.
    • Fletcher, A., McAuliffe, M., Lansford, K., Sinex, D., & Liss, J. (2017). Predicting intelligibility gains in individuals with dysarthria from baseline speech features.Journal of Speech, Language, and Hearing Research, 60, 3043–3057. https://doi.org/10.1044/2016_JSLHR-S-16-0218
    • Fox, C. M., & Boliek, C. A. (2012). Intensive voice treatment (LSVT LOUD) for children with spastic cerebral palsy and dysarthria.Journal of Speech, Language, and Hearing Research, 55(3), 930–945.
    • Han, W., Chan, C.-F., Choy, C.-S., & Pun, K.-P. (2006). An efficient MFCC extraction method in speech recognition. Paper presented at the 2006 IEEE International Symposium on Circuits and Systems, 2006 (ISCAS 2006, Proceedings), Kos, Greece.
    • Khan, T., Westin, J., & Dougherty, M. (2014). Classification of speech intelligibility in Parkinson's disease.Biocybernetics and Biomedical Engineering, 34(1), 35–45.
    • Liss, J. M., LeGendre, S., & Lotto, A. J. (2010). Discriminating dysarthria type from envelope modulation spectra.Journal of Speech, Language, and Hearing Research, 53(5), 1246–1255.
    • Lowell, S. Y., Colton, R. H., Kelley, R. T., & Hahn, Y. C. (2011). Spectral- and cepstral-based measures during continuous speech: Capacity to distinguish dysphonia and consistency within a speaker.Journal of Voice, 25(5), e223–e232.
    • Lowit, A., Dobinson, C., Timmins, C., Howell, P., & Kröger, B. (2010). The effectiveness of traditional methods and altered auditory feedback in improving speech rate and intelligibility in speakers with Parkinson's disease.International Journal of Speech-Language Pathology, 12(5), 426–436.
    • Mahler, L. A., & Ramig, L. O. (2012). Intensive treatment of dysarthria secondary to stroke.Clinical Linguistics & Phonetics, 26(8), 681–694.
    • McAuliffe, M. J., Fletcher, A. R., Kerr, S. E., O'Beirne, G. A., & Anderson, T. (2017). Effect of dysarthria type, speaking condition, and listener age on speech intelligibility.American Journal of Speech-Language Pathology, 26(1), 113–123.
    • McAuliffe, M. J., Kerr, S. E., Gibson, E. M., Anderson, T., & LaShell, P. J. (2014). Cognitive–perceptual examination of remediation approaches to hypokinetic dysarthria.Journal of Speech, Language, and Hearing Research, 57(4), 1268–1283.
    • Neel, A. T. (2009). Effects of loud and amplified speech on sentence and word intelligibility in Parkinson disease.Journal of Speech, Language, and Hearing Research, 52(4), 1021.
    • Paja, M. O. S., & Falk, T. H. (2012). Automated dysarthria severity classification for improved objective intelligibility assessment of spastic dysarthric speech. Paper presented at the 13th Annual Conference of the International Speech Communication Association, Portland, OR.
    • Pilon, M. A., McIntosh, K. W., & Thaut, M. H. (1998). Auditory vs visual speech timing cues as external rate control to enhance verbal intelligibility in mixed spastic ataxic dysarthric speakers: A pilot study.Brain Injury, 12(9), 793–803.
    • Sapir, S., Spielman, J., Ramig, L. O., Hinds, S. L., Countryman, S., Fox, C., & Story, B. (2003). Effects of intensive voice treatment (the Lee Silverman Voice Treatment [LSVT]) on ataxic dysarthria: A case study.American Journal of Speech-Language Pathology, 12(4), 387–399.
    • Sussman, J. E., & Tjaden, K. (2012). Perceptual measures of speech from individuals with Parkinson's disease and multiple sclerosis: Intelligibility and beyond.Journal of Speech, Language, and Hearing Research, 55(4), 1208–1219.
    • Tanner, K., Roy, N., Ash, A., & Buder, E. H. (2005). Spectral moments of the long-term average spectrum: Sensitive indices of voice change after therapy?.Journal of Voice, 19(2), 211–222.
    • Tjaden, K., Sussman, J. E., Liu, G., & Wilding, G. (2010). Long-term average spectral (LTAS) measures of dysarthria and their relationship to perceived severity.Journal of Medical Speech-Language Pathology, 18(4), 125–133.
    • Tjaden, K., & Wilding, G. E. (2004). Rate and loudness manipulations in dysarthria: Acoustic and perceptual findings.Journal of Speech, Language, and Hearing Research, 47(4), 766–783.
    • Turner, G. S., Tjaden, K., & Weismer, G. (1995). The influence of speaking rate on vowel space and speech intelligibility for individuals with amyotrophic lateral sclerosis.Journal of Speech and Hearing Research, 38(5), 1001–1013.
    • Van Nuffelen, G., De Bodt, M., Vanderwegen, J., Van de Heyning, P., & Wuyts, F. (2010). Effect of rate control on speech production and intelligibility in dysarthria.Folia Phoniatrica et Logopaedica, 62(3), 110–119.
    • Van Nuffelen, G., De Bodt, M., Wuyts, F., & Van de Heyning, P. (2009). The effect of rate control on speech rate and intelligibility of dysarthric speech.Folia Phoniatrica et Logopaedica, 61(2), 69–75.
    • Van Nuffelen, G., Middag, C., De Bodt, M., & Martens, J. P. (2009). Speech technology‐based assessment of phoneme intelligibility in dysarthria.International Journal of Language & Communication Disorders, 44(5), 716–730.
    • Vergin, R., O'shaughnessy, D., & Farhat, A. (1999). Generalized Mel frequency cepstral coefficients for large-vocabulary speaker-independent continuous-speech recognition.IEEE Transactions on Speech and Audio Processing, 7(5), 525–532.
    • Wisler, A., Berisha, V., Liss, J., & Spanias, A. (2014). Domain invariant speech features using a new divergence measure. Paper presented at the 2014 IEEE Spoken Language Technology Workshop (SLT), Lake Tahoe, NV.

    Additional Resources