Related Experiment Video
Updated: Jun 2, 2026

06:04
Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
Published on: March 24, 2023
Development of a Mandarin monosyllable test material with homogenous items (I): homogeneity selection
1Department of Otolaryngology/Head and Neck Surgery, Chinese PLA Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China.
Acta Oto-Laryngologica
|May 4, 2011
Summary
Researchers developed phonemically balanced Mandarin speech recognition test lists. A homogeneity selection process identified 342 monosyllables, creating a valuable resource for speech testing.
Area of Science:
- Audiology
- Speech Science
- Linguistics
Background:
- Developing standardized speech recognition test materials is crucial for accurate hearing assessments.
- Existing Mandarin monosyllabic test lists may lack sufficient phonemic balance and item homogeneity.
Purpose of the Study:
- To create succinct, phonemically balanced Mandarin monosyllabic recognition test lists.
- To ensure good item homogeneity for reliable speech recognition testing.
Main Methods:
- Developed a Phoneme Allocation Table based on Chinese phoneme distribution.
- Selected 489 monosyllables, organized into 30 lists (25 per list).
- Applied logistic regression and specific criteria (R value > 0.9, slope 2-12%/dB, threshold -8 to 10 dB HL) to screen for homogenous items.
Main Results:
- Established a framework of 30 phonemically balanced monosyllabic lists.
- Screened out 342 monosyllables demonstrating good homogeneity.
- These selected monosyllables can form highly sensitive test lists.
Conclusions:
- A homogeneity selection process is effective for creating uniform Mandarin monosyllabic test lists.
- The identified monosyllables provide a robust resource for Chinese Mandarin speech recognition testing.
More Related Videos
Related Concept Videos
Test for Homogeneity
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...
Goodness-of-Fit Test
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
Complementation Tests
A complementation test is a simple cross to identify whether the two mutations are located on the same gene or different genes. It was first performed by Edward Lewis in the 1940s while working on fruit flies. He developed the test to identify the location and arrangement of different mutations on chromosomes.
Organisms heterozygous for different mutations are crossed pairwise in all combinations. If present on different genes, the mutations can complement each other by providing the missing...
Organisms heterozygous for different mutations are crossed pairwise in all combinations. If present on different genes, the mutations can complement each other by providing the missing...
Hypothesis Test for Test of Independence
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
H0: The two variables (factors)...
McNemar's Test
McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
Introduction to Test of Independence
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
The test statistic for a test of independence is similar to that of a goodness-of-fit test:

