PHPCLANCHILDES with regular expressions

7 matches in 7 utterances

5.52 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (248 utterances, 1,268 words searched).

  1. how's your pants ? MOT · eng_USA_Brown/sarah123.cha, line 266 · Sarah 4;09
  2. how about just dripping them like this (be)cause they're gonna get all ov… MEL · eng_USA_Brown/sarah123.cha, line 361 · Sarah 4;09
  3. how do you look ? MEL · eng_USA_Brown/sarah123.cha, line 470 · Sarah 4;09
  4. how would this look ? GAI · eng_USA_Brown/sarah123.cha, line 492 · Sarah 4;09
  5. how does that look ? GAI · eng_USA_Brown/sarah123.cha, line 504 · Sarah 4;09
  6. how's that ? MOT · eng_USA_Brown/sarah123.cha, line 908 · Sarah 4;09
  7. I wonder how this would look in milk . MOT · eng_USA_Brown/sarah123.cha, line 928 · Sarah 4;09
  1. MOTsure you do .
    CHIDaddy ?
    MOT*MOT: how's your pants ?
    CHIfallin(g) down .
    MOTyeah .
  2. CHInow # I # got ta put # some in here .
    CHIwhoop .
    MEL*MEL: how about just dripping them like this (be)cause they're gonna get all over the table .
    CHII don't like that .
    CHIstart all over .
  3. GAIwhat do you see ?
    CHIme # with a chocolatey face .
    MEL*MEL: how do you look ?
    CHIchocolatey .
    MOTI'm tellin(g) ya .
  4. MOTyou'll choke .
    CHIoh .
    GAI*GAI: how would this look ?
    GAIshall we put these on you ?
    CHI&y uh # if you know how .
  5. CHI<hee hee hee hee hee hee> [/] hee .
    CHI<hee hee hee hee hee> [/] hee .
    GAI*GAI: how does that look ?
    CHIdem # horrible .
    CHIall cockedy@c .
  6. CHIsome # huh ?
    MOTthere .
    MOT*MOT: how's that ?
    CHIgood .
    CHInow add three cups .
  7. CHIone # two # three # four .
    CHInow # four .
    MOT*MOT: I wonder how this would look in milk .
    MELred milk ?
    MOTyeah # they have the strawberry syrup .

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. Sarah eng_USA_Brown5.52
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
eng_USA_Brown/sarah123.chaSarah 4;9.041,268775.52

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. how7 · 100.0%