#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#
CS
proLékaře.cz / Journals / The Clinician and Technology Journal / 2019 - 3

EFFECT OF AGE AND GENDER ON ARTICULATION OF VOICED AND VOICELESS STOP CONSONANTS IN CZECH

Download PDF English version

Authors: Martin Kaňok;  Michal Novotný
Authors‘ workplace: Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
Published in: Lékař a technika - Clinician and Technology No. 3, 2019, 49, 97-101
Category:

Overview

Evaluation of precision of consonant articulation is commonly used metric in assessment of pathological speech. However, up to date most of the research on consonant characteristics was performed on English while there are obvious language-specific differences. The aim of the current study was therefore to investigate the patterns of consonant articulation in Czech across 6 stop consonants with respect to age and gender. The database used consisted of 30 female and 30 male healthy participants (mean age 51.0 years, standard deviation 18.0 years and range from 20 to 79 years). Four acoustic variables including voice onset time (VOT), VOT ratio and two spectral moments were analyzed. The Czech plosives /p/, /t/ and /k/ were found to be characterized by short voicing lag (average VOT ranged from 14 to 32 ms) while voiced plosives /b/, /d/ and /g/ by long voicing lead (average VOT ranged from -79 to -91 ms). Furthermore, we observed significantly longer duration of both VOT (p < 0.05) and VOT ratio (p < 0.01) of voiceless plosives in female compared to male gender. Finally, we revealed a significant negative correlation between age and duration of voiceless (r = -0.36, p < 0.05) as well as voiced VOT (r = -0.45, p = 0.01) in female but not in male participants.

Klíčová slova:

ageing – articulation of consonant – voice onset time – spectral moment – acoustic analysis – Czech

Sources
  1. Mueller PB. What is normal aging?. Geriatric Medicine Today. 1985; 41: 48–57.
  2. Ramig LA and Scherer R. Acoustic analysis of voice of patients with neurological disease: rationale and preliminary data, Annals of Otology, Rhinology & Laryngology. 1988; 97: 164–172. DOI: 10.1177/000348948809700214
  3. Rusz J, Saft C, Schlegel U, Hoffman R and Skodda S. Phonatory Dysfunction as a Preclinical Symptom of Huntington Disease. Plos One. 2014; 9: e113412. DOI: 10.1371/journal.pone.0113412
  4. Hlavnicka J, Cmejla R, Tykalova T, Sonka K, Ruzicka E and Rusz J. Automated analysis of connected speech reveals early biomarkers of Parkinson’s disease in patients with rapid eye movement sleep behaviour disorder. Scientific Reports. 2017; 7: 12. DOI: 10.1038/s41598-017-00047-5
  5. Postuma RB, Lang AE, Gagnon JF, Pelletier A, Montplaisir JY. How does parkinsonism start? Prodromal parkinsonism motor changes in idiopathic REM sleep behaviour disorder. Brain. 2012; 135: 1860–1870. DOI: 10.1093/brain/aws093
  6. Kent RD and Read C. The acoustic analysis of speech. San Diego: Singular Pub. Group 1992.
  7. Fischer E and Goberman AM. Voice onset time in Parkinson disease. Journal of Communication Disorders. 2010: 43(1): 21–34. DOI: 10.1016/j.jcomdis.2009.07.004
  8. Novotny M, Rusz J, Cmejla R and Ruzicka E. Automatic Evaluation of Articulatory Disorders in Parkinson's Disease. IEEE-ACM Transactions on Audio Speech and Language Processing. 2014; 22(9): 1366–1378. DOI: 10.1109/TASLP.2014.2329734
  9. Auzou P, Ozsancak C, Morris RJ, Jan M, Eustache F and Hannequin D. Voice onset time in aphasia, apraxia of speech and dysarthria: a review. Clinical Linguistic & Phonetics. 2000; 14(2): 131–150. DOI: 10.1080/026992000298878
  10. Bortolini U, Zmarich C, Fior R and Bonifacio S. Word-initial voicing in the production of stops in normal and preterm Italian infants. International Journal of Pediatric Otorhinolaryngology. 1995; 31: 191–206. DOI: 10.1016/0165-5876(94)01091-B
  11. Petrosino L, Colcord RD, Kurcz KB and Yonker RJ. Voice onset time of velar stop productions in aged speakers. Journal of Perceptual and Motor Skills. 1993; 76: 83–88. DOI: 10.2466/pms.1993.76.1.83
  12. Morris RJ, McCrea CR and Herring KD. Voice onset time differences between adult males and females: Isolated syllables. Journal of Phonetics. 2008; 36(2): 308–317. DOI: 10.1016/j.wocn.2007.06.003
  13. Torre III P and Barlow JA. Age-related changes in acoustic characteristics of adult speech. Journal of Communication Disorders. 2009; 42: 324–333. DOI: 10.1016/j.jcomdis.2009.03. 001
  14. Baum SR and Ryan L. Rate of speech in aphasia: Voice onset time. Brain and Language. 1993; 44: 431–445. DOI: 10.1006/brln.1993.1026
  15. Sweeting PM, Baker RJ. Voice onset time in a normal aged population. Journal of speech, hearing Research. 1982; 25: 129–134. DOI: 10.1044/jshr.2501.129
  16. Lundeborg I, Larsson M, Wiman S and McAllister AM. Voice onset time in Swedish children and adults. Logopedics Phoniatrics Vocology. 2012; 37(3): 117–122. DOI: 10.3109/14015439.2012.664654
  17. Decoster W and Debruyne F. Changes in spectral measures and voice-onset time with age: a cross-sectional and a longitudinal study. Folia Phoniatricaet Logopaedica. 1997; 49 (6): 269–80. DOI: 10.1159/000266467
  18. Bona J. Voice onset time and speakers' age: data from Hungarian. Clinical Linguistic & Phonetics. 2014; 28(5): 366–72. DOI: 10.3109/02699206.2013.875593
  19. Robb M, Gilbert H and Lerman J. Influence of gender and environmental setting on VOT. Folia Phoniatricaet Logopaedica. 2005; 57: 125–133. DOI: 10.1159/000084133
  20. Boersma P. Praat, a system for doing phonetics by computer. Glot International. 2002; 5: 341–345.
  21. Simackova S, Podlipsky VJ and Chladkova K. Czech spoken in Bohemia and Moravia. Journal of the International Phonetic Association. 2012; 42(2): 225–232. DOI: 10.1017/S0025100312000102
  22. Rosner BS, Lopez-Bascuas LE, Garcia-Albea JE and Fahey RP. Voice-onset times for Castilian Spanish initialstops. Journal of Phonetics. 2000; 28: 217–224. DOI: 10.1006/jpho.2000.0113
  23. Ogut F, Kilic MA, Engin EZ and Midilli R. Voice onset times for Turkish stop consonants. Speech Communication. 2006; 48(9): 1094–1099. DOI: 10.1016/j.specom.2006.02.003
  24. Rusz J, Novotny M, Hlavnicka J, Tykalova T and Ruzicka E. High-accuracy voice-based classification between patients with Parkinson’s disease and other neurological diseases may be an easy task with inappropriate experimental design. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017; 25: 1319–1321. DOI: 10.1109/TNSRE.2016.2621885
Labels
Biomedicine
THE TIME DELAY OF AIR/OXYGEN MIXTURE DELIVERY AFTER THE CHANGE OF SET FIO2: AN IMPROVEMENT OF A NEONATAL MATHEMATICAL MODEL
THE FRACTAL DIMENSION-BASED DESCRIPTOR OF THE MICROVASCULAR PATTERN ON THE HISTOLOGICAL SECTION
GOLF SWING VARIABILITY IN ELITE FEMALE JUNIOR GOLFERS
APPLICATION OF THE ARTEC EVA SCANNER FOR ORTHOTICS IN PRACTICE
Article was published in

The Clinician and Technology Journal

Issue 3
2019 Issue 3
Most read in this issue
Journals
Login
Forgotten password

Enter the email address that you registered with. We will send you instructions on how to set a new password.

Login

Don‘t have an account?  Create new account

#ADS_BOTTOM_SCRIPTS#