PHPCLANCHILDES with regular expressions

7 matches in 7 utterances

18.23 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (113 utterances, 384 words searched).

  1. can we make a tower # Zeke ? MOT · eng_USA_Bates/zeke20.cha, line 16 · Zeke 1;08
  2. wan(t) (t)a make a tower ? MOT · eng_USA_Bates/zeke20.cha, line 20 · Zeke 1;08
  3. wan(t) (t)a make a tower ? MOT · eng_USA_Bates/zeke20.cha, line 22 · Zeke 1;08
  4. you wan(t) (t)a make a tower ? MOT · eng_USA_Bates/zeke20.cha, line 27 · Zeke 1;08
  5. make a tower ? MOT · eng_USA_Bates/zeke20.cha, line 32 · Zeke 1;08
  6. want me to make a tower again ? MOT · eng_USA_Bates/zeke20.cha, line 75 · Zeke 1;08
  7. want me to make a tower again ? MOT · eng_USA_Bates/zeke20.cha, line 83 · Zeke 1;08
  1. eng_USA_Bates/zeke20.cha, line 16 · Zeke, 1;8.
    MOTwan(t) (t)a play with those ?
    MOTstack [/] stack (th)em up ?
    MOT*MOT: can we make a tower # Zeke ?
    CHI0 .
    MOTwan(t) (t)a make a tower ?
  2. eng_USA_Bates/zeke20.cha, line 20 · Zeke, 1;8.
    MOTcan we make a tower # Zeke ?
    CHI0 .
    MOT*MOT: wan(t) (t)a make a tower ?
    MOTwan(t) (t)a make a tower ?
    MOThmm ?
  3. eng_USA_Bates/zeke20.cha, line 22 · Zeke, 1;8.
    CHI0 .
    MOTwan(t) (t)a make a tower ?
    MOT*MOT: wan(t) (t)a make a tower ?
    MOThmm ?
    MOTyou wan(t) (t)a make a tower ?
  4. eng_USA_Bates/zeke20.cha, line 27 · Zeke, 1;8.
    MOTwan(t) (t)a make a tower ?
    MOThmm ?
    MOT*MOT: you wan(t) (t)a make a tower ?
    CHI0 .
    MOTmake a tower ?
  5. eng_USA_Bates/zeke20.cha, line 32 · Zeke, 1;8.
    MOTyou wan(t) (t)a make a tower ?
    CHI0 .
    MOT*MOT: make a tower ?
    MOT0 [=! gasps] .
    CHIoh !
  6. eng_USA_Bates/zeke20.cha, line 75 · Zeke, 1;8.
    MOThmm ?
    CHI0 .
    MOT*MOT: want me to make a tower again ?
    MOThmm ?
    CHI0 .
  7. eng_USA_Bates/zeke20.cha, line 83 · Zeke, 1;8.
    MOThmm ?
    CHI0 .
    MOT*MOT: want me to make a tower again ?
    CHIuh !
    MOTyou gon (t)a <le(t)> [//] wait until I get it done ?

Matches by age

One dot per transcript: matches per 1,000 words of everyone but CHI. Dots of the same child are joined.

Matches by child

Matches per 1,000 words, all transcripts of each child together, highest first.

  1. Zeke eng_USA_Bates18.23
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
eng_USA_Bates/zeke20.chaZeke 1;8.3847718.23

What the pattern matched

1 different forms (lowercased). Useful to see what a pattern really catches: unexpected forms here usually mean the pattern is too broad.

  1. make7 · 100.0%