Analysis 03

FACE lowering, MOUTH fronting, LOT~THOUGHT merging.

Axel Bohmann https://www.freinem.uni-freiburg.de/mitglieder/dr-axel-bohmann-alu-englisches-seminar , Lars Hinrichs https://larshinrichs.site
10-09-2020

Data

As in previous documents, we have imported the data such as it was at the end of analysis_02, and it is now loaded and available as df_long and df_wide.

Tense~lax mergers before /l/

The relevant pairs - FLEECE-KIT, FACE-DRESS, and GOOSE-FOOT, all preceding /l/ - are so rare that I would venture we shouldn’t include them in the global datasets.

The onset of FACE

Speaker-level

For this feature, we can conduct the speaker-level analysis by simply asking: who has the lowest FACE centroid at 20%, i.e. the highest F1 value?

df_long %>% 
  filter(timepoint == 20,
         vowel == "FACE") %>% 
  group_by(name) %>% 
  summarise(face_onset = mean(f1_lobanov),
            dataset = nth(dataset, 1)) %>% 
  mutate(face_onset = (face_onset + 1.1)) %>% 
  #-----viz-----#
  ggplot(aes(x = reorder(name, face_onset),
             y = face_onset,
             fill = dataset)) +
  geom_col(stat = "identity",
           width = 0.5)          +
  scale_fill_grey(start = 0,
                  end = 0.8)     +
  coord_flip()                   +
  labs(
    title = "Lowering of the FACE onset",
    x = NULL,
    y = "normalized F1 at 20% duration") +
  theme_classic(base_family = "Roboto Condensed")

The darker shades here, as per the legend, are for the newer datasets. And while the tendency is not overwhelmingly clear, one could say that the higher F1-values are more clearly provided by bars in the lightest shade, i.e. by the older speakers.

Token-level

We’ll append F1 at 20% to a dedicated col. called face_lowering in df_wide for all rows where vowel == "FACE".

df_wide <- df_wide %>% 
  mutate(face_lowering = 
           ifelse(vowel == "FACE", f1_lobanov_20, NA))

MOUTH fronting

This feature is a very clear marker of southernness. Very conservative. It lies in the frontness of F2 at 20%.

Speaker-level

df_long %>% 
  filter(timepoint == 20,
         vowel == "MOUTH") %>% 
  group_by(name) %>% 
  summarise(mouth_fronting = mean(f2_lobanov),
            dataset = nth(dataset, 1)) %>% 
  mutate(mouth_fronting = (mouth_fronting + 0.9)) %>% 
  #-----viz-----#
  ggplot(aes(x = reorder(name, mouth_fronting),
             y = mouth_fronting,
             fill = dataset)) +
  geom_col(stat = "identity",
           width = 0.5)          +
  scale_fill_grey(start = 0,
                  end = 0.8)     +
  coord_flip()                   +
  labs(
    title = "Fronting of MOUTH",
    x = NULL,
    y = "normalized F2 at 20% duration") +
  theme_classic(base_family = "Roboto Condensed")

You can call me a simpleton if you like, but I think this picture looks very similar to the one for FACE lowering above.

Token-level

Let’s add the value for F2 at 20% to the right of df_wide for all rows where vowel == MOUTH.

df_wide <- df_wide %>% 
  mutate(mouth_fronting = 
           ifelse(vowel == "MOUTH", f2_lobanov_20, NA))

LOT~THOUGHT Merger

Narrowing down the LOT class

DARLA distinguishes between THOUGHT and other low-back vowels, but those are all rolled together… which is why we have a class “LOT/PALM/START”. In order to really study the LOT~THOUGHT phenomenology in an epistemologically sound way, let’s run some exclusions.

Running those exclusions, we end up with the following members of the LOT/PALM/START class.

lotwords <- 
  df_long %>% 
  filter(timepoint == 50,
         vowel == "LOT/PALM/START",
         !fol_seg %in% c("R", "L"), 
         # lexical exclusions
         !word %in% c("FATHER", "FATHER'S",
         "FATHERS", "LAGER", "MAH", "LAS", 
         "MILAN"),
         # misspellings
         !word %in% c("",
                      "WAH", "WAS",
                      "WASN'T", "WASN'TT",
                      "FATEHR"), 
         !str_detect(word, "AU"),
         !str_detect(word, "AL"),
         !str_detect(word, "A$")
         ) %>% 
  count(word) 

lotwords %>%
  kbl() %>% 
  kable_paper("striped")
word n
ACCOMPLISHED 1
ACCOMPLISHMENTS 1
ADOPTED 1
ADOPTING 1
ADOPTS 1
AWE 2
AWFUL 7
AWFULLY 1
BACL 1
BEYOND 15
BLAH 5
BLOCK 13
BLOCKED 2
BLOCKS 3
BLOCKSTREET 2
BLOKS 1
BODIES 1
BODY 1
BODY’S 1
BOGS 4
BOMB 44
BOND 2
BONDING 3
BONDNESS 1
BOSS 1
BOTHER 63
BOTHERED 1
BOTHERING 1
BOTHERS 2
BOTTLE 6
BOTTOM 4
BOX 1
CHAVEZ 3
CHICAGO 2
CHICANO 1
CHOPPY 1
CHOREOGRAPHY 1
CLOCK 1
CLOSET 6
COB 2
COBS 54
COCK 47
COFFEE 1
COLORADO 4
COM 2
COMMENTS 1
COMMON 17
COMMONS 1
COMMUNE 4
COMMUNISM 1
COMMUNIST 1
COMPLEX 2
COMPLEXES 2
COMPLICATED 7
CONCEPT 1
CONDOS 1
CONFIDENCE 2
CONFIDENT 6
CONFLICT 1
CONGRESS 8
CONGRESSMEN 1
CONNELY 1
CONRETE 1
CONSTANT 3
CONTEXT 1
CONTROVERSIES 1
COOPERATIVES 1
COP 3
COPS 1
CORRESPONDENCE 3
COST 5
COT 49
COTTAGE 2
COTTON 1
CROCKETT 1
CROP 3
CROPS 1
DOCTOR 1
DOCTOR’S 4
DOCTORATE 1
DOD 1
DOGGET 1
DOGS 60
DON 41
DONKEY 47
DONT 2
DONW 1
DOT 1
DROP 57
DROPPED 5
DROPPING 1
DROPS 14
ECONOMIC 6
ECONOMY 11
ECONOMY’S 1
FOG 4
FOGGY 49
FONTS 2
FORGOT 1
FOSTER 1
FROG 5
FROGS 3
GAH 1
GARAGE 9
GEOGRAPHY 1
GOD 10
GODS 13
GOSSIP 1
GOT 7
GROVEL 4
HAH’D 13
HOBBY 1
HOCK 48
HOD 240
HOG 2
HOMOGENOUS 1
HONEST 6
HONESTLY 1
HOPPIN 40
HOPPING 16
HOSTILE 1
HOT 13
JOB 143
JOBS 11
JOG 1
JOHN 56
JOHNS 3
JOHNSON 4
JOHNSTON 3
JOPLIN 1
KNOCK 2
KNOCKED 1
LOBBY 1
LOCK 3
LOCKING 1
LOT 237
LOTS 74
MASSAGE 1
MODERATE 5
MODERATELY 522
MODERATLEY 1
MODERN 3
MODEST 2
MOM 22
MOM’S 6
MOMS 1
MONOTONY 1
NON 11
NOT 17
NOTCH 1
O’CLOCK 5
OBJECT 1
OBJECTS 1
OBVIOUS 1
OBVIOUSLY 3
ODD 52
ON 10
OP 1
OPPERATOR 1
OPPERTUNITY 2
OPPOSITE 2
OPTION 2
PATRIOTIC 1
PECAN 46
PHENOMENON 2
PHILOSOPHY 3
PHOTOGRAPHY 43
PLODDED 2
POCKET 4
PONDS 3
POP 106
POPCORN 101
POPERTY 1
POPPED 2
POPULAR 1
POSITIVE 5
POSSIBLE 4
POSSIBLY 2
POSSUMS 4
POT 15
POTTY 2
POVERTY 1
PRAGUE 1
PREDOMINANTLY 1
PREDOMINATELY 1
PROBABLY 103
PROBLEM 9
PROBLEMS 14
PROCESS 4
PROCESSED 2
PRODUCT 5
PRODUCTS 2
PROFITABLE 1
PROGRESS 106
PROJECT 3
PROJECTS 2
PROPER 1
PROPERLY 1
PROPERTIES 1
PROPERTY 24
PROPERY 1
PROPSING 1
PROSECUTED 1
REMODELED 1
ROBBLE 1
ROBERT 2
ROBOT 1
ROCK 6
ROCKS 3
RODDENBERRY 1
RODS 4
ROSS 2
ROT 45
ROTC 2
ROTTED 2
ROTTEN 59
SCOTT 1
SHOCK 2
SHOP 3
SHOPPING 1
SHOT 9
SHOTS 2
SLOT 1
SNOBBISH 1
SOCCER 3
SOD 46
SOFT 1
SPONSERS 1
SPOTS 1
SQUATTED 3
STOC 1
STOCK 56
STOP 62
STOPPED 3
TOD 1
TOM 2
TOMAS 3
TOMAS’S 1
TOMMY 1
TOP 7
TOPOGRAPHY 1
TRODDED 1
TROTTED 1
UNGODLY 1
UPON 19
WANT 54
WANTS 2
WASH 4
WASHINGTON 16
WATCH 11
WATCHED 60
WATCHING 7

So those are our LOT-words. I’ll place them in a vector and use that to filter out the rows of interest for the LOT~THOUGHT analysis.

lotwords <- lotwords %>% 
  pull(word)
lotwords %>% str()

 chr [1:252] "ACCOMPLISHED" "ACCOMPLISHMENTS" "ADOPTED" ...

Define the dataset

dat <- df_long %>% 
  filter(vowel == "THOUGHT" |
           word %in% lotwords,
         timepoint == 50) %>% 
  mutate(vowel = recode(vowel, "LOT/PALM/START" = "LOT")) %>% 
  filter(vowel %in% c("THOUGHT", "LOT"))

Speaker-level

Get speaker-level scores

speakers <- df_long %>% 
  pull(name) %>% 
  unique()

pillai_lotthought <- tibble()
wins <- c()
fails <- c()

for (s in (1:length(speakers))){
  # cat("\nspeaker no. ", s)
  dat_s <- dat %>%
    filter(name == speakers[s]) %>% 
    mutate(f1_lobanov =
           as.numeric(f1_lobanov),
           f2_lobanov =
           as.numeric(f2_lobanov))
  
  v1 <- unique(dat_s$vowel)[1]
  n_v1 <- dat_s %>% filter(vowel == v1) %>% nrow()
  if (n_v1 < 6) next
  
  v2 <- unique(dat_s$vowel)[2]
  n_v2 <- dat_s %>% filter(vowel == v2) %>% nrow()
  if (n_v2 < 6) next
  
  if (length(unique(dat_s$vowel)) == 2 &
      length(unique(dat_s$vowel))) {
      my_man <- manova(cbind(f1_lobanov,
                             f2_lobanov) ~ vowel,
                       data = dat_s)
      pil <- summary(my_man, tol=0)$stats["vowel",
                                   "Pillai"]
      # wins <- append(wins, s)
      # n_success <- length(wins)
      # cat("\nsuccess", n_success, ":", 
      #     s, ":",
      #     pil)
      
  } else {
    # fails <- append(fails, s)  
    # cat("\nfail", length(fails), ":", 
    #     s)
    next
    }
  
  newscore <- tibble(name = speakers[s],
                     lotthought_pillai = pil)
  
  pillai_lotthought <-
    bind_rows(pillai_lotthought, newscore) %>% 
    arrange(desc(lotthought_pillai)) %>% 
    unique()
}

pillai_lotthought

# A tibble: 160 x 2
   name          lotthought_pillai
   <chr>                     <dbl>
 1 un-1987-dcl-f             0.885
 2 un-1987-jak-m             0.811
 3 un-1987-wyf-m             0.763
 4 un-1987-gb-m              0.743
 5 un-1987-ffw-f             0.739
 6 un-1987-aam-m             0.716
 7 un-1987-hcb-m             0.692
 8 un-1987-dr2-f             0.677
 9 un-1987-blb-m             0.672
10 un-1987-is-f              0.666
# … with 150 more rows

Visualize speaker-level scores

datas <- df_long %>% 
  group_by(name) %>% 
  summarise(dataset = nth(dataset, 1)) %>% 
  select(name, dataset)

pillai_lotthought %>% 
  left_join(datas) %>% 
  ggplot(aes(x = reorder(name, lotthought_pillai),
             y = lotthought_pillai,
             fill = dataset))    +
  geom_col(stat = "identity",
           width = 0.5)          +
  scale_fill_grey(start = 0,
                  end = 0.8)     +
  coord_flip()                   +
  labs(
    title = "Merging of LOT and THOUGHT",
    x = NULL,
    y = "pillai index score") +
  theme_classic(base_family = "Roboto Condensed")

Token-level

We’ll add the distance of each LOT or THOUGHT token from the centroid of the OTHER vowel to two to-be-created columns at the right of df_wide.

Obtain LOT and THOUGHT centroids for each speaker

# define speakers again, this time with dataset
speakers <- df_long %>% 
  select(name, dataset) %>% 
  unique()

# make empty results obj for all speakers
lot_and_thoughts <- tibble()

# get results
for (s in (1:nrow(speakers))){
  
  # get speaker-level info
  speaker <- speakers %>% 
    pull(name) %>% 
    nth(s)
  dataset <- speakers %>% 
    pull(dataset) %>% 
    nth(s)
  
  # get LOT centroid
  lot_s <- dat %>% 
    getspeakervowel(speaker, "LOT")  %>% 
    unlist()
  
  # get THOUGHT centroid
  thought_s <- dat %>% 
    getspeakervowel(speaker, "THOUGHT")  %>% 
    unlist()
  
  results_s <- 
    c(speaker, dataset, lot_s, thought_s)
  
  names(results_s) <- c("name", "dataset",
                        "f1_lot", "f2_lot",
                        "f1_thought", "f2_thought")
  
  # append speaker results to results
  lot_and_thoughts <- bind_rows(lot_and_thoughts,
                                results_s) %>% 
    na.omit() 
}

(lot_and_thoughts <- 
  lot_and_thoughts %>% 
  mutate(across(starts_with("f"), as.numeric)))

# A tibble: 290 x 6
   name         dataset f1_lot f2_lot f1_thought f2_thought
   <chr>        <chr>    <dbl>  <dbl>      <dbl>      <dbl>
 1 hi-2009-ab-f hi       0.899 -0.754      0.833      -1.34
 2 hi-2009-al-f hi       1.15  -0.860      0.735      -1.13
 3 hi-2009-cf-f hi       1.15  -0.962      0.445      -1.30
 4 hi-2009-er-f hi       0.832 -0.730      0.494      -1.11
 5 hi-2009-jg-m hi       1.27  -0.915      0.362      -1.42
 6 hi-2009-kg-f hi       1.00  -0.906      0.198      -1.49
 7 hi-2009-mr-f hi       1.11  -0.874      0.389      -1.37
 8 hi-2009-nc-f hi       1.07  -0.928      0.209      -1.27
 9 hi-2009-ro-m hi       1.26  -0.917      0.448      -1.33
10 hi-2010-ak-m hi       1.01  -0.886      0.455      -1.34
# … with 280 more rows

Get token-level distance measure and attach to df_wide

Get the token-level distance measure.

dat <- dat %>% 
  left_join(lot_and_thoughts) %>% 
  mutate(
    ed_from_thought = ifelse(vowel == "LOT", 
                           cdis(f1_lobanov, f1_thought, 
                                f2_lobanov, f2_thought), 
                           NA),
  ed_from_lot = ifelse(vowel == "THOUGHT", 
                           cdis(f1_lobanov, f1_lot, 
                                f2_lobanov, f2_lot), 
                           NA)
  )

The distance measures are now contained in dat. We’ll reduce it to only the columns vowelID, ed_from_lot and ed_from_thought. And then join it with df_wide.

dat <- 
  dat %>% 
  select(vowelID, ed_from_lot, ed_from_thought) 

df_wide <- 
  df_wide %>%
  left_join(dat)

Save data

Save the data sheets. Include suffix _03 in the filename to indicate that this version of the data is produced by the document analysis_03.Rmd.

df_long %>% export("../../_data/data_long_03.RDS")
df_wide %>% export("../../_data/data_wide_03.RDS")

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

Bohmann & Hinrichs (2020, Oct. 9). Originalitätsverdacht | Bohmann, Bohmann & Hinrichs: Analysis 03. Retrieved from https://actuation.netlify.app/posts/analysis-03/

BibTeX citation

@misc{bohmann2020analysis,
  author = {Bohmann, Axel and Hinrichs, Lars},
  title = {Originalitätsverdacht | Bohmann, Bohmann & Hinrichs: Analysis 03},
  url = {https://actuation.netlify.app/posts/analysis-03/},
  year = {2020}
}