`autoscore` =========== > R Package: 0.4.0 <br> Shiny App: > [autoscore.usu.edu](http://autoscore.usu.edu) *Authors:* - *Tyson S. Barrett* - *Stephanie A. Borrie* - *Sarah E. Yoho* 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 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. 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`. #### 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: - `select_cols()` – The first function which takes the data and gets it in the right format for analysis. - `split_clean()` – Using the cleaned data from `select_cols()`, this uses `stringr` to turn the phrases into individual words. - `alternate_fun()` – If a data.frame of alternate spellings is provided, this function will find and normalize all alternate spellings to match the original spelling as defined by the researcher. - `match_position_basic()` – This function is the workhorse of the package. It takes the cleaned data and does three main things: 1) applies all the rules except for the `position_rule`, 2) finds the matches between the responses and the targets, and 3) reports how far away the matches are from each other. - `count_matches()` – Finally, this function takes the information from `match_position_basic()` and counts the number of matches based on the `position_rule`. 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 observation. Use of the Online Tool ---------------------- Visit [autoscore.usu.edu](http://autoscore.usu.edu) to use the online tool. Instructions for its use are found there. <img src="man/figures/online_autoscore_snapshot.png" align="center" width="70%" height="70%" /> Use of the R Package -------------------- To install the package use the developmental version as it is not yet on CRAN. ``` r 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. ``` r library(tidyverse) library(autoscore) data("example_data") example_data #> # A tibble: 40 x 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. ``` r example_data %>% autoscore() %>% ## using all the defaults as.tibble() ## to shorted output #> # A tibble: 40 x 6 #> id target response human autoscore equal #> <dbl> <fct> <fct> <dbl> <int> <lgl> #> 1 1 mate denotes a judgem… made the dinner in it 1 0 FALSE #> 2 1 rampant boasting capt… rubbed against the c… 1 1 TRUE #> 3 1 resting older earring resting alert hearing 1 1 TRUE #> 4 1 bolder ground from ju… boulder down from dr… 2 1 FALSE #> 5 1 remove and name for s… remember the name fo… 3 2 FALSE #> 6 1 done with finest hand… dinner finished hand… 1 1 TRUE #> 7 1 support with dock and… 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… i can for hookah kno… 2 2 TRUE #> 10 1 connect the beer devi… connected beard kind… 1 0 FALSE #> # … with 30 more rows ``` Next, let’s change some of the rules. ``` r example_data %>% autoscore(plural_rule = FALSE, tense_rule = FALSE) %>% as.tibble() #> # A tibble: 40 x 6 #> id target response human autoscore equal #> <dbl> <fct> <fct> <dbl> <int> <lgl> #> 1 1 mate denotes a judgem… made the dinner in it 1 0 FALSE #> 2 1 rampant boasting capt… rubbed against the c… 1 1 TRUE #> 3 1 resting older earring resting alert hearing 1 1 TRUE #> 4 1 bolder ground from ju… boulder down from dr… 2 1 FALSE #> 5 1 remove and name for s… remember the name fo… 3 2 FALSE #> 6 1 done with finest hand… dinner finished hand… 1 1 TRUE #> 7 1 support with dock and… 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… i can for hookah kno… 2 2 TRUE #> 10 1 connect the beer devi… 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. ``` r example_data %>% autoscore(output = "none") #> # A tibble: 40 x 10 #> id target response human diff_target_pre diff_response_p… diff_target #> <dbl> <list> <list> <dbl> <list> <list> <list> #> 1 1 <chr … <chr [5… 1 <int [4]> <int [5]> <lgl [4]> #> 2 1 <chr … <chr [4… 1 <int [3]> <int [4]> <lgl [3]> #> 3 1 <chr … <chr [3… 1 <int [3]> <int [3]> <lgl [3]> #> 4 1 <chr … <chr [4… 2 <int [4]> <int [4]> <lgl [4]> #> 5 1 <chr … <chr [5… 3 <int [5]> <int [5]> <lgl [5]> #> 6 1 <chr … <chr [3… 1 <int [4]> <int [3]> <lgl [4]> #> 7 1 <chr … <chr [6… 1 <int [5]> <int [6]> <lgl [5]> #> 8 1 <chr … <chr [5… 2 <int [4]> <int [5]> <lgl [4]> #> 9 1 <chr … <chr [5… 2 <int [5]> <int [5]> <lgl [5]> #> 10 1 <chr … <chr [4… 1 <int [4]> <int [4]> <lgl [4]> #> # … with 30 more rows, and 3 more variables: diff_response <list>, #> # count_target <int>, 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. ``` r autoscore::acceptable_spellings #> # A tibble: 257 x 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. ``` r example_data %>% autoscore::autoscore(acceptable_df = autoscore::acceptable_spellings) %>% as.tibble() #> # A tibble: 40 x 6 #> id target response human autoscore equal #> <dbl> <fct> <fct> <dbl> <int> <lgl> #> 1 1 mate denotes a judgem… made the dinner in it 1 0 FALSE #> 2 1 rampant boasting capt… rubbed against the c… 1 1 TRUE #> 3 1 resting older earring resting alert hearing 1 1 TRUE #> 4 1 bolder ground from ju… boulder down from dr… 2 2 TRUE #> 5 1 remove and name for s… remember the name fo… 3 3 TRUE #> 6 1 done with finest hand… dinner finished hand… 1 1 TRUE #> 7 1 support with dock and… 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… i can for hookah kno… 2 2 TRUE #> 10 1 connect the beer devi… 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). ### Learn More Peer-reviewed publications and further tutorials are forthcoming. For more information, contact autoscorehelp@gmail.com.