Relative Fundamental Frequency (RFF)

Introduction

RFF is a novel measure used to assess excessive laryngeal muscle tension, a significant factor in many voice disorders. Learn more about the science and methods underpinning this exciting research!

Description

Excessive laryngeal muscle tension is a significant factor in a various voice disorders, affecting up to half of cases in multidisciplinary voice clinics.1,2 Despite its prevalence, clinical assessment remains challenging due to unreliable auditory impressions,3,4 non-specific manual palpation,5,6 and limited precision in standard acoustic measures.

To address this gap, we have proposed relative fundamental frequency (RFF) as an acoustic estimate of laryngeal tension. RFF has shown differences between people with and without vocal hyperfunction,7,8 Parkinson’s disease,9 and laryngeal dystonia.10 It normalizes in people with vocal hyperfunction after successful voice therapy8,11 and relates to changes in vocal effort, subglottal pressure, and listener perceptions of vocal strain,12 as well as indirect measurements of laryngeal tension in typical speakers.13 Changes in RFF have also been observed in high voice users after prolonged voice use, even without perceptual voice changes.14

Stimuli and Audio Examples

Recommended stimuli for RFF analysis: Our work has shown that “uniform utterances” (rather than running speech) with two vowels (e.g., /a/, /i/, /u/) surrounding an /f/ or a /ʃ/ result in the lowest within-speaker standard deviations.15 Further, producing these utterances with equal stress on the vowels results in the lowest within-speaker standard deviation.16 For each speaker, we recommend collecting the at least nine RFF “instances” with equal stress on the vowels: /afa afa afa ifi ifi ifi ufu ufu ufu/. Listen to an example of these stimuli below.

Methods for Calculating RFF

Manual Analysis: A trained technician can manually calculate RFF by interacting with acoustic waveform displays to determine individual periods of each of the 10 cycles before and after the consonant. When manually calculating RFF, our work has shown that at least six samples are required to reach a stable estimate.10 Calculating RFF has required manual interaction with waveform displays to determine individual periods of each of the 10 cycles before and after the consonant. Because of the difficulties in determining the exact onset and offset of voicing, a trained technician is required. 

We have developed an internal training protocol for manually estimating RFF, which can be found here.*

*The folder contains a tutorial for estimating RFF in the free-to-use acoustic analysis software, Praat, in addition to a training regime. Instructions for the training can be found in the Excel document of the folder, with corresponding sound samples in the “sound files” directory.

Automated Analysis: Learn about the aRFF-AP and accelerometer-based algorithms used to analyze voice data.

  • Microphone Signals: Accurate and fast RFF estimation methods initially developed by Dr. Yu-An (Stephanie) Lien17 and later updated by Dr. Jenny Vojtech to account for differences in voice sample characteristics.18 Download the most recent version of the algorithm here and its instructions for use here!
  • Accelerometer Signals: Novel techniques leveraging wearable technology based on neck-surface accelerometry, developed by Dr. Matti Groll.19 Download it here and its instructions for use here!

Funding

The National Institute on Deafness and Other Communication Disorders (NIDCD) provided the majority of funding for this research. Please feel free to use these algorithms in scientific research!

When using our automated RFF algorithms, please cite as follows:

  • Microphone signals:18 “RFF values were calculated using an automated MATLAB program; algorithm details can be found in Vojtech et al. (2019).”
  • Accelerometer signals:19 “RFF values were calculated using an automated MATLAB program; algorithm details can be found in Groll et al. (2020).”

Our lab is continuing to refine these algorithms, so we anticipate new versions to be available in the future.

Feedback

Do you have any questions, comments, or suggestions about RFF? Feel free to leave feedback here.

Disclaimer

We do not recommend use of these algorithms or RFF as clinical outcome measures at this time. However, we hope to discover with the use of these faster algorithms whether there is a role for RFF in clinical voice assessment.

References

  1. Dworkin-Valenti, J. P., Stachler, R. J., Stern, N., & Amjad, E. H. (2018). Pathophysiologic perspectives on muscle tension dysphonia. Archives of Otolaryngology and Rhinology, 4(1), 1–10.
  2. Roy, N. (2003). Functional dysphonia. Current Opinion in Otolaryngology & Head and Neck Surgery, 11(3), 144–148.
  3. Dejonckere, P. H., Remacle, M., Fresnel-Elbaz, E., Woisard, V., Crevier-Buchman, L., & Millet, B. (1996). Differentiated perceptual evaluation of pathological voice quality: Reliability and correlations with acoustic measurements. Revue de Laryngologie-Otologie-Rhinologie, 117(3), 219–224.
  4. Zraick, R. I., Kempster, G. B., Connor, N. P., Thibeault, S., Klaben, B. K., Bursac, Z., Thrush, C. R., & Glaze, L. E. (2011). Establishing validity of the Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V). American Journal of Speech-Language Pathology, 20(1), 14–22.
  5. Milstein, C. F. (1999). Laryngeal function associated with changes in lung volume during voice and speech production in normal speaking women [Doctoral Dissertation]. The University of Arizona.
  6. Stager, S. V., Bielamowicz, S. A., Regnell, J. R., Gupta, A., & Barkmeier, J. M. (2000). Supraglottic activity: Evidence of vocal hyperfunction or laryngeal articulation? Journal of Speech, Language, and Hearing Research, 43(1), 229–238.
  7. Stepp, C. E., Hillman, R. E., & Heaton, J. T. (2010). The impact of vocal hyperfunction on relative fundamental frequency during voicing offset and onset. Journal of Speech, Language, and Hearing Research, 53(5), 1220-1226.
  8. Roy, N., Mazin, A., & Awan, S. N. (2016). Automated acoustic analysis of task dependency in adductor spasmodic dysphonia versus muscle tension dysphonia. The Laryngoscope, 127(6), 1402-1407.
  9. Stepp, C. E. (2013). Relative fundamental frequency during vocal onset and offset in older speakers with and without Parkinson’s disease. The Journal of the Acoustical Society of America, 133(3), 1637-1643.
  10. Eadie, T. L., & Stepp, C. E. (2013). Acoustic correlate of vocal effort in spasmodic dysphonia. Annals of Otology, Rhinology & Laryngology, 122(3), 169-176.
  11. Stepp, C. E., Merchant, G. R., Heaton, J. T., & Hillman, R. E. (2011). Effects of voice therapy on relative fundamental frequency during voicing offset and onset in patients with vocal hyperfunction. Journal of Speech, Language, and Hearing Research, 54(5), 1260-1266.
  12. Lien, Y. A. S., Michener, C. M., Eadie, T. L., & Stepp, C. E. (2015). Individual monitoring of vocal effort with relative fundamental frequency: Relationships with aerodynamics and listener perception. Journal of Speech, Language, and Hearing Research, 58(3), 566-575.
  13. McKenna, V. S., & Stepp, C. E. (2016). The relationship between acoustical and perceptual measures of vocal effort. The Journal of the Acoustical Society of America, 139(3), 1223-1233.
  14. Heller Murray, E. S., Hands, G. L., Calabrese, C. R., & Stepp, C. E. (2016). Effects of adventitious acute vocal trauma: Relative fundamental frequency and listener perception. Journal of Voice, 30(6), 753.e7-753.e15.
  15. Lien, Y. S., Gattuccio, C. I., & Stepp, C. E. (2014). Effects of phonetic context on relative fundamental frequency. Journal of Speech, Language, and Hearing Research, 57(4), 1259-1267.
  16. Park, Y., & Stepp, C. E. (2019). The effects of stress type, vowel identity, baseline f0, and loudness on the relative fundamental frequency of vowels. Journal of Speech, Language, and Hearing Research, 62(8), 2563-2576.
  17. Lien, Y. A. S., Calabrese, C. R., Michener, C. M., Murray, E. H., Van Stan, J. H., Mehta, D. D., Hillman, R. E., Noordzij, J. P., & Stepp, C. E. (2017). Validation of an algorithm for semi-automated estimation of voice relative fundamental frequency. Journal of the Acoustical Society of America, 146(5), 3184-3197.
  18. Vojtech, J. M., Segina, R. K., Buckley, D. P., Kolin, K. R., Tardif, M. C., Noordzij, J. P., & Stepp, C. E. (2019). Refining algorithmic estimation of relative fundamental frequency: Accounting for sample characteristics and fundamental frequency estimation method. Journal of the Acoustical Society of America, 146(5), 3184-3202.
  19. Groll, M. D., Vojtech, J. M., Hablani, S., Mehta, D. D., Buckley, D. P., Noordzij, J. P., & Stepp, C. E. (2020). Automated relative fundamental frequency algorithms for use with neck-surface accelerometer signals. Journal of Speech, Language, and Hearing Research, 63(11), 3587-3600.