README.md

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autoscore

R Package: 0.5.0 Shiny App: autoscore.usu.edu

Authors:

The purpose of autoscore is to automatically score word identification in speech perception research, such as studies involving listener understanding of speech in background noise or disordered speech. The article first presenting the program has been cited in 20 peer-reviewed publications as of June 2022 (see Google Scholar).

The program uses a flexible number of rules that determine whether a response set of words (i.e., listener transcriptions) match a target set of words (i.e., speech corpus). At the most basic level, Autoscore counts words in the listener transcript as correct if they match the words in the target phrase exactly (regardless of word order). Individual rules can be applied or removed, depending on the needs of researcher and the scoring rules of the research lab. Examples of rules available in Autoscore include the ability to count as correct substitutions of articles (“a” for “the”) or differences in plural or tense (adding -s or -ed to a word). Additional rules can be added by the researcher as needed.

The rule options are categorized into either spelling rules or grammar rules.

Spelling Rules

  1. acceptable_spell_rule: Response word counted correct if it is a homophone or common misspelling of the target word, according to a preloaded default acceptable spelling list (contains over 300 common acceptable spellings). User can also download the default acceptable spelling list, add/remove items, and upload for automation. Response word counted correct if is on the acceptable spelling list. This rule is activated by providing the acceptable spelling list. Default is FALSE.
  2. root_word_rule: Response word counted correct if the target word (e.g. ‘day’) is embedded at either the beginning (e.g. ‘daybreak’) of the target word. Default is FALSE.
  3. double_letter_rule: Response word counted correct if it omitted a double letter within a word (e.g. ‘atack’ matches ‘attack’) or added an unnecessary double letter (e.g. ‘occassion’ matches ‘occasion’). Default is FALSE.
  4. number_text_rule: Response word counted correct if using actual numbers (e.g. 1, 2, 100) instead of the spelled out version (e.g. one, two, one hundred). Default is FALSE.
  5. contractions_rule: Response word counted correct if using the contraction of the target (e.g., target is “she will” and the response is “she’ll”). This rule is activated by providing contractions to use (there is a default list provided). Default is FALSE.
  6. compound_rule: Response word counted correct if matching a specified compound. This rule is activated by providing a named vector (e.g., c("junkyard" = "junk yard") where “junkyard” is the target but some responses were “junk yard”).

Grammar Rules

  1. tense_rule: Response word counted correct if it differs from the target word only by tense; Default is FALSE.
  2. tense_add_rule: Response word counted correct if it only adds a tense as compared to the target word; Default is FALSE. It is also referred to as “Tense+ Rule.”
  3. plural_rule: Response word counted correct if it differs from the target word only by plurality; Default is FALSE.
  4. plural_add_rule: Response word counted correct if it only adds a plural as compared to the target word; Default is FALSE. It is also referred to as “Plural+ Rule.”
  5. a_the_rule: Substitutions between “a” and “the” to be scored as correct; Default is FALSE.

Design

The API of the R package is simple. A single call to autoscore() with the formatted data will run everything for you. This function is a composite of several sub-functions that do various jobs:

Beyond the main analysis when using autoscore(), we can also call pwc() to get the percent words correct (based on the number of target words) for each id.

Use of the Online Tool

Visit autoscore.usu.edu to use the online tool. Instructions for its use are found there.

Use of the R Package

To install the package use the developmental version as it is not yet on CRAN.

remotes::install_github("autoscore/autoscore")

An example of the use of autoscore is below. We will use the example data set provided in the package.

library(tidyverse)
library(autoscore)

data("example_data")
example_data
#> # A tibble: 40 × 4
#>       Id Target                      Response                    human
#>    <dbl> <chr>                       <chr>                       <dbl>
#>  1     1 mate denotes a judgement    made the dinner in it           1
#>  2     1 rampant boasting captain    rubbed against the captain      1
#>  3     1 resting older earring       resting alert hearing           1
#>  4     1 bolder ground from justice  boulder down from dresses       2
#>  5     1 remove and name for stake   remember the name for steak     3
#>  6     1 done with finest handle     dinner finished handle          1
#>  7     1 support with dock and cheer she put the duck in chair       1
#>  8     1 or spent sincere aside      earth bent spent her aside      2
#>  9     1 account for who could knock i can for hookah knock          2
#> 10     1 connect the beer device     connected beard kindle bus      1
#> # … with 30 more rows

First, let’s use all the defaults and look at the first 10 rows of the output.

example_data %>%
  autoscore() %>%   ## using all the defaults
  as_tibble()       ## to shorted output
#> # A tibble: 40 × 6
#>       id target                      response              human autoscore equal
#>    <dbl> <chr>                       <chr>                 <dbl>     <dbl> <lgl>
#>  1     1 mate denotes a judgement    made the dinner in it     1         0 FALSE
#>  2     1 rampant boasting captain    rubbed against the c…     1         1 TRUE 
#>  3     1 resting older earring       resting alert hearing     1         1 TRUE 
#>  4     1 bolder ground from justice  boulder down from dr…     2         1 FALSE
#>  5     1 remove and name for stake   remember the name fo…     3         2 FALSE
#>  6     1 done with finest handle     dinner finished hand…     1         1 TRUE 
#>  7     1 support with dock and cheer she put the duck in …     1         0 FALSE
#>  8     1 or spent sincere aside      earth bent spent her…     2         2 TRUE 
#>  9     1 account for who could knock i can for hookah kno…     2         2 TRUE 
#> 10     1 connect the beer device     connected beard kind…     1         0 FALSE
#> # … with 30 more rows

Next, let’s change some of the rules.

example_data %>%
  autoscore(plural_rule = FALSE, tense_rule = FALSE) %>%
  as_tibble()
#> # A tibble: 40 × 6
#>       id target                      response              human autoscore equal
#>    <dbl> <chr>                       <chr>                 <dbl>     <dbl> <lgl>
#>  1     1 mate denotes a judgement    made the dinner in it     1         0 FALSE
#>  2     1 rampant boasting captain    rubbed against the c…     1         1 TRUE 
#>  3     1 resting older earring       resting alert hearing     1         1 TRUE 
#>  4     1 bolder ground from justice  boulder down from dr…     2         1 FALSE
#>  5     1 remove and name for stake   remember the name fo…     3         2 FALSE
#>  6     1 done with finest handle     dinner finished hand…     1         1 TRUE 
#>  7     1 support with dock and cheer she put the duck in …     1         0 FALSE
#>  8     1 or spent sincere aside      earth bent spent her…     2         2 TRUE 
#>  9     1 account for who could knock i can for hookah kno…     2         2 TRUE 
#> 10     1 connect the beer device     connected beard kind…     1         0 FALSE
#> # … with 30 more rows

We can also change the output type to “none” to get all the data from the computation.

example_data %>%
  autoscore(output = "none")
#> # A tibble: 40 × 10
#>       id target    response  human diff_target_pre diff_response_pre diff_target
#>    <dbl> <list>    <list>    <dbl> <list>          <list>            <list>     
#>  1     1 <chr [4]> <chr [5]>     1 <int [4]>       <int [5]>         <lgl [4]>  
#>  2     1 <chr [3]> <chr [4]>     1 <int [3]>       <int [4]>         <lgl [3]>  
#>  3     1 <chr [3]> <chr [3]>     1 <int [3]>       <int [3]>         <lgl [3]>  
#>  4     1 <chr [4]> <chr [4]>     2 <int [4]>       <int [4]>         <lgl [4]>  
#>  5     1 <chr [5]> <chr [5]>     3 <int [5]>       <int [5]>         <lgl [5]>  
#>  6     1 <chr [4]> <chr [3]>     1 <int [4]>       <int [3]>         <lgl [4]>  
#>  7     1 <chr [5]> <chr [6]>     1 <int [5]>       <int [6]>         <lgl [5]>  
#>  8     1 <chr [4]> <chr [5]>     2 <int [4]>       <int [5]>         <lgl [4]>  
#>  9     1 <chr [5]> <chr [5]>     2 <int [5]>       <int [5]>         <lgl [5]>  
#> 10     1 <chr [4]> <chr [4]>     1 <int [4]>       <int [4]>         <lgl [4]>  
#> # … with 30 more rows, and 3 more variables: diff_response <list>,
#> #   count_target <dbl>, count_response <int>

To use the acceptable spelling rule, let’s use the default provided in autoscore. . In the data frame below, the target spellings are the generally accepted spellings that are in the target list of words while the acceptable column are those that should also be counted as correct.

autoscore::acceptable_spellings
#> # A tibble: 257 × 2
#>    target   acceptable       
#>    <chr>    <chr>            
#>  1 absent   abcent           
#>  2 achieved achieved, achived
#>  3 acid     asid             
#>  4 advance  advanse          
#>  5 again    agin             
#>  6 alone    aloan            
#>  7 along    a long           
#>  8 among    amung            
#>  9 ancient  ansient          
#> 10 ancient  anceint          
#> # … with 247 more rows

Using this, we can provide it to the autoscore() function with the acceptable_df argument.

example_data %>%
  autoscore::autoscore(acceptable_df = autoscore::acceptable_spellings) %>%
  as_tibble()
#> # A tibble: 40 × 6
#>       id target                      response              human autoscore equal
#>    <dbl> <chr>                       <chr>                 <dbl>     <dbl> <lgl>
#>  1     1 mate denotes a judgement    made the dinner in it     1         0 FALSE
#>  2     1 rampant boasting captain    rubbed against the c…     1         1 TRUE 
#>  3     1 resting older earring       resting alert hearing     1         1 TRUE 
#>  4     1 bolder ground from justice  boulder down from dr…     2         2 TRUE 
#>  5     1 remove and name for stake   remember the name fo…     3         3 TRUE 
#>  6     1 done with finest handle     dinner finished hand…     1         1 TRUE 
#>  7     1 support with dock and cheer she put the duck in …     1         0 FALSE
#>  8     1 or spent sincere aside      earth bent spent her…     2         2 TRUE 
#>  9     1 account for who could knock i can for hookah kno…     2         2 TRUE 
#> 10     1 connect the beer device     connected beard kind…     1         0 FALSE
#> # … with 30 more rows

In each of these examples, it is clear that the human and “autoscore” agree the majority of the time. The times that they disagree, it is usually predictably a human error or a subjective judgement that the researcher will have to consider (for example by including alternate spellings of words as we just demonstrated).

Finally, we can use the pwc() function to calculate the percent words correct using the output from autoscore(). We provide the id variable so that pwc() provides a value for each individual.

example_data %>%
  autoscore() %>% 
  pwc(id)
#> # A tibble: 2 × 2
#>      id   pwc
#>   <dbl> <dbl>
#> 1     1  32.7
#> 2     2  20.8

As of autoscore 0.5.0 two new rules can also be used: number_text_rule (changes numbers like 1 or 2 to “one” or “two”) and contractions_df (which, when applied, adjusts for contraction words). There is also a new function called compound_fixer() that allows you to adjust compound words the way you want. The example below shows all three of these new features together. To start we make a fake data set with some good examples of things we want to fix.

small_example <- tibble::tribble(
  ~id, ~target, ~response, ~human,
  1, "the coin ate it", "a coins for it", 1,
  2, "beat the clock", "beets the clock", 2,
  3, "beated it", "beet it", 1,
  4, "beets the clock", "beat the clock", 2,
  5, "beeted the clock", "beet the clock", 2,
  6, "junkyard", "junk yard", 1,
  7, "Breakfast is great", "break fast is great", 3,
  8, "The matches are on the shelf", "23  the matches are on the shelf", 6,
  9, "The puppy played with a ball", "1  x", 0,
  10, "One two three", "1 2 3", 3,
  11, "Hide one thing", "hide 1", 2,
  12, "She will eat", "She'll eat", 3
)

Then, using this data, we apply the compound fixer function and the number and contraction rules. For the compound_fixer(), we want “junk yard” to become “junkyard” and “break fast” to become “breakfast”. These words are not in the default list so we add them with the comp argument. The human column is the correct number so we can compare autoscore’s results with it.

small_example %>% 
  dplyr::mutate(response = compound_fixer(response, comp = c("junkyard" = "junk yard", "breakfast" = "break fast"))) %>% 
  autoscore(number_text_rule = TRUE,
            contractions_df = autoscore::contractions_df) 
#>    id                       target                         response human
#> 1   1              the coin ate it                   a coins for it     1
#> 2   2               beat the clock                  beets the clock     2
#> 3   3                    beated it                          beet it     1
#> 4   4              beets the clock                   beat the clock     2
#> 5   5             beeted the clock                   beet the clock     2
#> 6   6                     junkyard                         junkyard     1
#> 7   7           Breakfast is great               breakfast is great     3
#> 8   8 The matches are on the shelf 23  the matches are on the shelf     6
#> 9   9 The puppy played with a ball                             1  x     0
#> 10 10                One two three                            1 2 3     3
#> 11 11               Hide one thing                           hide 1     2
#> 12 12                 She will eat                       She'll eat     3
#>    autoscore equal
#> 1          1  TRUE
#> 2          2  TRUE
#> 3          1  TRUE
#> 4          2  TRUE
#> 5          2  TRUE
#> 6          1  TRUE
#> 7          3  TRUE
#> 8          6  TRUE
#> 9          0  TRUE
#> 10         3  TRUE
#> 11         2  TRUE
#> 12         3  TRUE

Learn More

For more information, contact autoscorehelp@gmail.com.



autoscore/autoscore documentation built on July 24, 2022, 12:04 p.m.