1. Words in puddles of sound
Padraic Monaghan
University of York
Morten Christiansen
Cornell University

2. Words in a “sea of sound” (Saffran, 2001)
Discovering words
from continuous speech
with no reliable cues to word boundaries (Jones, 1918; Liberman et al., 1967)
where words are realised variably (Pollack & Pickett, 1964)

3. Segmentation and sublexical cues
Final syllables of words are longer (Klatt, 1975)
hamster v. ham (Saffran, Newport, & Aslin (1996; Salverda & McQueen, 2004):
First syllables of words are stressed
~60% of the time in English (Crystal & House, 1990; Pierrehumbert, 1981)
Johnson & Jusczyk (2001); Thiessen & Saffran (2003)
Certain diphones are more likely to occur across words than within words (Mattys et al., 2005)

4. Multiple cues in speech segmentation
Hierarchical model
(Mattys, White, &
Melhorn, 2005)

5. whosalovelybabyyesyouareyourealovelybabyarentyouyesyouare
Puddles
whosalovelybabyyesyouareyourealovelybabyarentyouyesyouare
In 5.5M words of child-directed speech:
Utterance Length
Percentage of Corpus 1 2 3 4 5 6 7 8
> 8
26.2
13.7
13.1
11.8
9.5
7.5
5.6
3.9
8.6

6. Lexical approach to segmentation
Once you’ve got the words, segmentation is easy (Norris, 1994; 2007)
Assume each utterance is a word
until you know differently
if it’s repeated, you keep it
if it doesn’t occur again, you lose it

7. Aims of Modelling
Utterances can’t be used as don’t know when it’s a single word, when it’s multiple (Brent & Cartwright, 1996)
utterance boundaries are sufficient to get started
single-word utterances are useful anchors for segmentation
It is possible to distinguish (most) single-word from (most) multiple word utterances
Proper nouns have a special role
Frequent multiple-word sequences will be “lexicalised” (Tomasello, 2001)

8. Lexical approach to segmentation
Familiar words used for segmentation by “Maggie” (Bortfeld et al., 2005):
“maggie’s bike had big, black wheels”
“hannah’s cup was bright and shiny”
infants familiarised to “bike” more quickly than “cup”
Proper nouns often occur as single utterances:
3.3% of utterances in “naomi” corpus in CHILDES
Very high frequent words are useful for categorising content words (Monaghan, Chater, & Christiansen, 2005; Redington, Chater, & Finch, 1998)

9. Corpora 6 corpora from CHILDES:
child-directed speech to children aged < 2:6
Orthographic transcription run through festival speech synthesiser (Black et al., 1990)
Corpus
Utterances
Words
MLU
Reference
Eve
18,280
62,734
3.43
Brown, 1973
Peter
21,311
74,185
3.48
Bloom et al, 1974
Nina
17,075
73,562
4.31
Suppes, 1974
Naomi
9,006
29,003
3.22
Sachs, 1983
Anne
28,250
96,008
3.49
Theakston et al., 2001
Aran
24,801
106,983

10. The model
kitty
thatsrightkitty
sayitagain
lookkitty
LEXICON

11. The model
kitty
thatsrightkitty
sayitagain
lookkitty
LEXICON

12. The model
kitty
thatsrightkitty
sayitagain
lookkitty
LEXICON
kitty 1.0

13. The model LEXICON kitty 0.99 kitty thatsrightkitty sayitagain
lookkitty
LEXICON
kitty

14. The model LEXICON kitty 0.99 kitty thatsrightkitty sayitagain
lookkitty
LEXICON
kitty

15. The model LEXICON kitty 1.99 thatsright 1.00 kitty thatsrightkitty
sayitagain
lookkitty
LEXICON
kitty
thatsright 1.00

16. The model LEXICON kitty 1.98 thatsright 0.99 kitty thatsrightkitty
sayitagain
lookkitty
LEXICON
kitty
thatsright 0.99

17. The model LEXICON kitty 2.98 thatsright 0.99 kitty thatsrightkitty
sayitagain
lookkitty
LEXICON
kitty
thatsright 0.99

18. The model LEXICON kitty 3.96 thatsright 0.97 sayitagain 0.99 look 1.00
thatsrightkitty
sayitagain
lookkitty
LEXICON
kitty
thatsright 0.97
sayitagain 0.99
look

19. More constraints in the model: Phonological glueoh
okay
noway
nevertheless
LEXICON oh
kay n
way
evertheless
Candidate words with recognised beginnings and endings admitted
Candidate words which divide a recognised word-internal diphone rejected

20. More constraints in the model: Phonological glueoh
okay no
nevertheless
GLUE
Beg: oh
End: oh
Glue: oh
LEXICON oh

21. More constraints in the model: Phonological glueoh
okay no
nevertheless
GLUE
Beg: oh
End: oh
Glue: oh
LEXICON oh

22. More constraints in the model: Phonological glueoh
okay no
nevertheless
GLUE
Beg: oh
End: oh
Glue: oh
LEXICON oh x
ka?
oh?
ok?

23. More constraints in the model: Phonological glueoh
okay no
nevertheless
GLUE
Beg: oh, ka
End: oh, ay
Glue: oh, ok, ka, ay
LEXICON oh
okay

24. More constraints in the model: Phonological glueoh
okay no
nevertheless
GLUE
Beg: oh, ka
End: oh, ay
Glue: oh, ok, ka, ay
LEXICON oh
okay no
nevertheless

25. Testing the model Decisions: Internal diphone glue constraint
Legal beginnings/endings constraint
Decay-rate
Ordering of lexicon…
Accuracy: Proportion of words segmented that are words
Completeness: Proportion of words that are segmented
Baseline segmentation: correct number of words in utterance, randomly positioned boundary (Brent & Cartwright, 1996)
Included
By length

26. Results: Accuracy
t(5) = , p < .0001

27. Results: Completeness
t(5) = , p < .0001

28. Results: Naomi’s Lexicon
Top 10 after 1K utterances:
Nomi
Say No
Yes
The
Okay
Whatsthis
Blanket Is
What

29. Results: Naomi’s Lexicon
Top 10 after 8K utterances:
You
Nomi
The It To
What I
That’s No
Your

30. Results: Naomi’s Lexicon
0.05 decay
Dashed line shows mean word length in corpus

31. Results: Naomi’s Lexicon
0.01 decay

32. Summary Model based on puddles of sound accurate, complete
reliance on Proper noun
frequent words “pop” out
same words useful for grammatical categorisation
No mechanism for alternative, competing parses of speech
first, cognitively plausible step for how lexicon may be generated
Relative role of phonological glue, legal boundaries, sorting by length/frequency