Investigating Prosodic Accommodation in Clinical Interviews with Depressed Patients

  • Brian VaughanEmail author
  • Carolina De Pasquale
  • Lorna Wilson
  • Charlie Cullen
  • Brian Lawlor
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 253)


Six in-depth clinical interviews, involving six elderly female patients (aged 60+) and one female psychiatrist, were recorded and analysed for a number of prosodic accommodation variables. Our analysis focused on pitch, speaking time, and vowel-space ratio. Findings indicate that there is a dynamic manifestation of prosodic accommodation over the course of the interactions. There is clear adaptation on the part of the psychiatrist, even going so far as to have a reduced vowel-space ratio, mirroring a reduced vowel-space ratio in the depressed patients. Previous research has found a reduced vowel-space ratio to be associated with psychological distress; however, we suggest that it indicates a high level of adaptation on the part of the psychiatrist and needs to be considered when analysing psychiatric clinical interactions.


Speech analysis Clinical interviews Depression Prosody Accommodation Interaction Vowel-space 


  1. 1.
    World Health Organization: Depression and other common mental disorders: global health estimates. Technical report, World Health Organization, Geneva (2017)Google Scholar
  2. 2.
    Smit, F., Shields, L., Petrea, I.: Preventing depression in the WHO European region. Technical report, World Health Organization (2016)Google Scholar
  3. 3.
    Strawbridge, R., Young, A.H., Cleare, A.J.: Biomarkers for depression: recent insights, current challenges and future prospects. Neuropsychiatr. Dis. Treat. 13, 1245–1262 (2017)CrossRefGoogle Scholar
  4. 4.
    Asgari, M., Shafran, I., Sheeber, L.B.: Inferring clinical depression from speech and spoken utterances. In: 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–5. IEEE, September 2014Google Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    De Looze, C., Oertel, C., Rauzy, S., Campbell, N.: Measuring dynamics of mimicry. In: ICPhS, vol. 1, pp. 1294–1297, August 2011Google Scholar
  7. 7.
    De Looze, C., Rauzy, S.: Measuring speakers’ similarity in speech by means of prosodic cues: methods and potential. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, pp. 1393–1396 (2011)Google Scholar
  8. 8.
    De Looze, C., Scherer, S., Vaughan, B., Campbell, N.: Investigating automatic measurements of prosodic accommodation and its dynamics in social interaction. Speech Commun. 58, 11–34 (2014)CrossRefGoogle Scholar
  9. 9.
    Moore II, E., Clements, M., Peifer, J., Weisser, L.: Analysis of prosodic variation in speech for clinical depression. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No. 03CH37439), vol. 3, pp. 2925–2928 (2003)Google Scholar
  10. 10.
    Cannizzaro, M.S., Harel, B., Reilly, N., Chappell, P., Snyder, P.J.: Voice acoustical measurement of the severity of major depression. Brain Cogn. 56, 30–35 (2004)CrossRefGoogle Scholar
  11. 11.
    Ozdas, A., Shiavi, R., Silverman, S., Silverman, M., Wilkes, D.: Analysis of fundamental frequency for near term suicidal risk assessment. In: SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. ‘Cybernetics Evolving to Systems, Humans, Organizations, and Their Complex Interactions’ (Cat. No. 00CH37166), vol. 3, pp. 1853–1858. IEEE (2000)Google Scholar
  12. 12.
    France, D.J., Shiavi, R.G., Silverman, S., Silverman, M., Wilkes, D.M.: Acoustical properties of speech as indicators of depression and suicidal risk. IEEE Trans. Biomed. Eng. 47, 829–837 (2000)CrossRefGoogle Scholar
  13. 13.
    Scherer, S., Morency, L.-P., Gratch, J., Pestian, J.: Reduced vowel space is a robust indicator of psychological distress: a cross-corpus analysis. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4789–4793. IEEE, April 2015Google Scholar
  14. 14.
    Scherer, S., Hammal, Z., Yang, Y., Morency, L.-P., Cohn, J.F.: Dyadic behavior analysis in depression severity assessment interviews. In: Proceedings of the 16th International Conference on Multimodal Interaction - ICMI 2014, pp. 112–119 (2014)Google Scholar
  15. 15.
    Levitan, R., Hirschberg, J.: Measuring acoustic-prosodic entrainment with respect to multiple levels and dimensions. In: Proceedings of Interspeech 2011, pp. 3081–3084. ISCA (2011)Google Scholar
  16. 16.
    Coulston, R., Oviatt, S., Darves, C.: Amplitude convergence in children’s conversational speech with animated personas. In: 7th International Conference on Spoken Language Processing, ICSLP 2002 - INTERSPEECH 2002 (2002)Google Scholar
  17. 17.
    Kousidis, S., Dorran, D., McDonnell, C., Coyle, E.: Times series analysis of acoustic feature convergence in human dialogues. In: Proceedings of Interspeech (2008)Google Scholar
  18. 18.
    Edlund, J., Heldner, M., Hirschberg, J.: Pause and gap length in face-to-face interaction. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, pp. 2779–2782 (2009)Google Scholar
  19. 19.
    Babel, M., Bulatov, D.: The role of fundamental frequency in phonetic accommodation. Lang. Speech 55, 231–248 (2011)CrossRefGoogle Scholar
  20. 20.
    Collins, B.: Convergence of fundamental frequencies in conversation: if it happens, does it matter? In: Proceedings of ICSLP, vol. 98, (1998)Google Scholar
  21. 21.
    Gregory, S.W., Webster, S.: A nonverbal signal in voices of interview partners effectively predicts communication accommodation and social status perceptions. J. Pers. Soc. Psychol. 70(6), 1231–1240 (1996)CrossRefGoogle Scholar
  22. 22.
    Heldner, M., Edlund, J.: Pauses, gaps and overlaps in conversations. J. Phon. 38, 555–568 (2010)CrossRefGoogle Scholar
  23. 23.
    Parrill, F., Kimbara, I.: Seeing and hearing double: the influence of mimicry in speech and gesture on observers. J. Nonverbal Behav. 30, 157–166 (2006)CrossRefGoogle Scholar
  24. 24.
    Pickering, M.J., Garrod, S.: Alignment as the basis for successful communication. Res. Lang. Comput. 4, 203–228 (2006)CrossRefGoogle Scholar
  25. 25.
    Boylan, P.: Accommodation Theory Revisited Again. Lingua e società, pp. 287–305 (2009)Google Scholar
  26. 26.
    Tickle-Degnen, L., Rosenthal, R.: The nature of rapport and its nonverbal correlates. Psychol. Inq. 1(4), 285–293 (1990)CrossRefGoogle Scholar
  27. 27.
    Shepard, C., Giles, H., Le Poire, B.: Communication accommodation theory. In: Robinson, W., Giles, H. (eds.) The New Handbook of Language and Social Psychology, pp. 33–56. Wiley, New York (2001)Google Scholar
  28. 28.
    Miles, L.K., Nind, L.K., Macrae, C.N.: The rhythm of rapport: interpersonal synchrony and social perception. J. Exp. Soc. Psychol. 45(3), 585–589 (2009)CrossRefGoogle Scholar
  29. 29.
    Vocavio (2017)Google Scholar
  30. 30.
    Degottex, G., Kane, J., Drugman, T., Raitio, T., Scherer, S.: COVAREP - a collaborative voice analysis repository for speech technologies. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 960–964. IEEE, May 2014Google Scholar
  31. 31.
    Kousidis, S., Dorran, D., McDonell, C., Coyle, E.: Time series analysis of acoustic feature convergence in human dialogues. In: Specom 2009, St. Petersburg, Russian Federation, pp. 1–6 (2009)Google Scholar
  32. 32.
    De Looze, C., Vaughan, B., Kelly, F., Kay, A.: Providing objective metrics of team communication skills via interpersonal coordination mechanisms. In: INTERSPEECH, Dresden, Germany, pp. 1–5 (2015)Google Scholar
  33. 33.
    Hamilton, M.: Development of a rating scale for primary depressive illness. Br. J. Soc. Clin. Psychol. 6, 278–96 (1967)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Brian Vaughan
    • 1
    Email author
  • Carolina De Pasquale
    • 1
  • Lorna Wilson
    • 2
  • Charlie Cullen
    • 3
  • Brian Lawlor
    • 4
  1. 1.Dublin Institute of TechnologyDublinIreland
  2. 2.St. James’s University Hospital DublinDublinIreland
  3. 3.University of the West of ScotlandHamiltonScotland
  4. 4.Trinity College DublinDublinIreland

Personalised recommendations