Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

7.2K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
7.2K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

524
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
524
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

941
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
941
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.5K
Behrens–Fisher Test00:57

Behrens–Fisher Test

245
The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
This test...
245
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.1K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Validity of the Cepstral Spectral Index of Dysphonia in the Persian Language in Sustained Vowel, Sentence, and All-Voiced Sentence Contexts.

Journal of speech, language, and hearing research : JSLHR·2026
Same author

Radiation-Induced Volume Changes in the Parotid and Submandibular Glands, Intragland Dose Distribution, and Quality of Life Scores Over a Long Follow-Up Period.

Advances in radiation oncology·2026
Same author

Self-Perceived Vocal Status in Airline Crew Before and After Flights of Different Routes.

Journal of voice : official journal of the Voice Foundation·2026
Same author

Characterizing nystagmus using a portable eye movement recorder in patients with Meniere's disease and MRI-confirmed endolymphatic hydrops.

Auris, nasus, larynx·2026
Same author

The Relationship Between the Acoustic Breathiness Index and Voice Handicap Index in Turkish Adults.

Journal of voice : official journal of the Voice Foundation·2026
Same author

Contribution of Fundamental Frequency to Perceived Vocal Roughness: Evidence From Time-Scaled Natural Voice Samples.

Journal of voice : official journal of the Voice Foundation·2026

Related Experiment Video

Updated: Jan 15, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

810

Cross-Validation of the Acoustic Roughness Index in German.

Itsuki Kitayama1, Kiyohito Hosokawa1, Bernhard Lehnert2

  • 1Department of Otorhinolaryngology and Head & Neck Surgery, The University of Osaka Graduate School of Medicine, Osaka, Japan.

Journal of Voice : Official Journal of the Voice Foundation
|October 8, 2025
PubMed
Summary

The Acoustic Roughness Index (ARI) shows good correlation with perceived vocal roughness in German speakers. This acoustic measure offers potential for voice quality assessment, but further research is needed for improved accuracy.

Keywords:
Acoustic analysisAcoustic roughness indexAuditory-perceptual judgmentRoughnessSubharmonics

More Related Videos

Assessment of Spatial Lingual Tactile Sensitivity using a Gratings Orientation Test
06:00

Assessment of Spatial Lingual Tactile Sensitivity using a Gratings Orientation Test

Published on: September 17, 2021

3.0K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

2.0K

Related Experiment Videos

Last Updated: Jan 15, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

810
Assessment of Spatial Lingual Tactile Sensitivity using a Gratings Orientation Test
06:00

Assessment of Spatial Lingual Tactile Sensitivity using a Gratings Orientation Test

Published on: September 17, 2021

3.0K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

2.0K

Area of Science:

  • Speech Science
  • Acoustic Analysis
  • Voice Disorders

Background:

  • Vocal roughness is a key indicator of voice quality and potential dysphonia.
  • Objective acoustic measures are needed to complement subjective perceptual assessments of roughness.

Purpose of the Study:

  • To validate the Acoustic Roughness Index (ARI) for German-speaking individuals.
  • To assess the correlation between ARI and perceived vocal roughness.
  • To determine the diagnostic accuracy of ARI in identifying rough voices.

Main Methods:

  • Recorded sustained vowels and continuous speech from 218 German speakers (dysphonia and healthy controls).
  • Perceptual roughness rated by three experienced clinicians using the Grade, Roughness, Breathiness, Asthenia, Strain (GRBAS) scale.
  • Acoustic analysis performed using the ARI algorithm in Praat software.
  • Concurrent validity assessed via Spearman correlation; diagnostic validity via ROC analysis.

Main Results:

  • Moderate intra-rater (κ=0.45) and fair inter-rater (κ=0.35) reliability for perceptual roughness ratings.
  • Significant correlation between ARI and perceived roughness (rs=0.726, P<0.001).
  • Good diagnostic accuracy for ARI (AUC=0.824) with an optimal threshold of 2.00 (71.8% sensitivity, 79.3% specificity).

Conclusions:

  • The Acoustic Roughness Index (ARI) shows promise as an objective acoustic measure for vocal roughness.
  • ARI demonstrates good concurrent and diagnostic validity in a German-speaking population.
  • Further research is recommended to enhance the accuracy and reliability of ARI for voice quality evaluation.