PHPCLANCHILDES with regular expressions

6 matches in 6 utterances

2.88 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (400 utterances, 2,083 words searched).

  1. should we write that too ? GAI · eng_USA_Brown/sarah124.cha, line 528 · Sarah 4;09
  2. that's a long word too # isn't it ? MEL · eng_USA_Brown/sarah124.cha, line 577 · Sarah 4;09
  3. well # not too bad . MOT · eng_USA_Brown/sarah124.cha, line 671 · Sarah 4;09
  4. I bet you forgot how to spell Daddy # too . MOT · eng_USA_Brown/sarah124.cha, line 806 · Sarah 4;09
  5. do you get clean # too ? MEL · eng_USA_Brown/sarah124.cha, line 985 · Sarah 4;09
  6. that was too tricky for me . MOT · eng_USA_Brown/sarah124.cha, line 1558 · Sarah 4;09
  1. eng_USA_Brown/sarah124.cha, line 528 · Sarah, 4;9.12
    MOT+" I love you ?
    CHI0 .
    GAI*GAI: should we write that too ?
    CHIwhat is that way down thing ?
    GAIthat's the line that you made .
  2. eng_USA_Brown/sarah124.cha, line 577 · Sarah, 4;9.12
    MELa very good job .
    CHInow I know how to spell somethin(g) .
    MEL*MEL: that's a long word too # isn't it ?
    MOTSarah # what was it you were sayin(g) yesterday about your Mother ?
    MOTwhat was it ?
  3. eng_USA_Brown/sarah124.cha, line 671 · Sarah, 4;9.12
    CHIok # if I know how .
    CHIthis is how you make a valentine ?
    MOT*MOT: well # not too bad .
    CHImm # I'll make another one on the other side .
    MOTalright .
  4. eng_USA_Brown/sarah124.cha, line 806 · Sarah, 4;9.12
    CHII forgot .
    MOTyou remembered Ann and Jerry though # huh ?
    MOT*MOT: I bet you forgot how to spell Daddy # too .
    CHIuh # I maked@n a bubble in my mouth xxx .
    MOTwho's a cute kid ?
  5. eng_USA_Brown/sarah124.cha, line 985 · Sarah, 4;9.12
    MELyou do what ?
    MELgiggle in the bath+tub ?
    MEL*MEL: do you get clean # too ?
    CHIwe mix it with hot (a)n(d) warm .
    MOTRichard # he's a little hellion .
  6. eng_USA_Brown/sarah124.cha, line 1558 · Sarah, 4;9.12
    CHIlet it go .
    CHInow I'm goin(g) play a tricky trick .
    MOT*MOT: that was too tricky for me .

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_Brown2.88
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
eng_USA_Brown/sarah124.chaSarah 4;9.122,083662.88

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. too6 · 100.0%