PHPCLANCHILDES with regular expressions

6 matches in 5 utterances

3.46 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (363 utterances, 1,733 words searched).

  1. there's Donna and there's Diane and Jo_Anne . MOT · eng_USA_Brown/sarah081.cha, line 873 · Sarah 3;10
  2. there's Donna and there's Diane and Jo_Anne . MOT · eng_USA_Brown/sarah081.cha, line 873 · Sarah 3;10
  3. and then there's thousands elsewhere . MEL · eng_USA_Brown/sarah081.cha, line 888 · Sarah 3;10
  4. I can't even name them there's so many . MOT · eng_USA_Brown/sarah081.cha, line 893 · Sarah 3;10
  5. then there's <Betty (a)n(d)> [/] # Betty (a)n(d) uh +... MOT · eng_USA_Brown/sarah081.cha, line 900 · Sarah 3;10
  6. wonder it there's any mail ? MOT · eng_USA_Brown/sarah081.cha, line 1296 · Sarah 3;10
  1. eng_USA_Brown/sarah081.cha, line 873 · Sarah, 3;10.09
    MOTwho's Donna ?
    MOTshe'll learn all their names .
    MOT*MOT: there's Donna and there's Diane and Jo_Anne .
    MELand they're all cousins ?
    MOTyeah .
  2. eng_USA_Brown/sarah081.cha, line 888 · Sarah, 3;10.09
    MELyou do !
    MOTthat's only here on th(e) cover .
    MEL*MEL: and then there's thousands elsewhere .
    MOToh .
    MOTI can't even name them there's so many .
  3. eng_USA_Brown/sarah081.cha, line 893 · Sarah, 3;10.09
    MELand then there's thousands elsewhere .
    MOToh .
    MOT*MOT: I can't even name them there's so many .
    CHIBetty .
    MOTyeah .
  4. eng_USA_Brown/sarah081.cha, line 900 · Sarah, 3;10.09
    CHIBetty .
    MOTyeah .
    MOT*MOT: then there's <Betty (a)n(d)> [/] # Betty (a)n(d) uh +...
    CHIGeorgie .
    MOTBetty (a)n(d) Georgie (a)n(d) Kathy up the street .
  5. eng_USA_Brown/sarah081.cha, line 1296 · Sarah, 3;10.09
    CHISandman (i)s in my eye .
    MOTthe Sandman (i)s in your eye .
    MOT*MOT: wonder it there's any mail ?
    MOThmm ?
    CHIuh .

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_Brown3.46
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
eng_USA_Brown/sarah081.chaSarah 3;10.091,733563.46

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. there's6 · 100.0%