Statistical Frequency in Word Segmentation

1. Statistical Frequency in Word Segmentation

2. Words don’t come with nice clean boundaries between them • Where are the word boundaries?

3. Question: How do children work out where the word boundaries are? • Statistical regularities There are several potential clues: • Pauses (although this is dubious) • Intonation (this too is dubious)

4. Statistical Regularities • Words very rarely begin with [dw], • Words never begin with [bn], • Words never begin with [lb], • Etc. • So if the child hears these sequences, the child hypothesizes the sequence occurred in the middle or at the end of the word.

5. Statistical Regularities • Voiceless stops that begin words are almost always aspirated, • Voiced segments that end words are often de-voiced, • Various other phonological processes may occur, e.g., word-final frication, etc. • So these are phonological clues that may help segment the speech stream.

6. Problem • In order for children to be able to make use of these cues, they must be able to track the frequency of such items in the speech, otherwise it is a useless cue. • So if the child is not able to track the frequency of [bn] at the beginning of words, what use is using this strategy?

7. Statistical Tracking • Very recent work suggests that children do in fact have the capacity to track statistical frequencies of certain elements in their environment. • Major researchers: Jenny Saffran (Wisconsin), Rebecca Gomez (Arizona), Elisa Newport (Rochester), Richard Aslin (Rochester), LouAnn Gerken (Arizona), Gary Marcus (NYU), etc.

8. The Experiment - Overview • Create a synthesized string of syllables that occur in a particular frequency (can’t use English…). • Expose the children to this string of syllables for ~20 minutes. • Test children to see if they have a preference for the highly frequent syllable sets or the rare syllable sets. • If children show a preference (no matter what direction that preference is in), then children are sensitive to frequencies of syllables in the input.

9. Sample Stimulus Their language consisted of: • Four consonants (p,t,b,d) • Three vowels (a, i, u) • Which when combined created 12 syllables (pa, ti, bu, da, etc.). • These then created six words: • babupu, bupada, dutaba, patubi, pidabu, and tutibu

10. ba 2 babu 1 bi bupu 1 1 bu bupa 4 1 ba bu pu pa pada 2 1 pa da bu 1 1 pi duta ta ba du pu taba 1 1 tu bi pa ta 1 patu 1 da bu pi 1 1 ti tubi ti bu tu tu pida 2 1 2 1 da dabu di tuti 0 1 du 1 tibu 1

11. Transitional Probabilities • The chances of a word containing bu are much greater than the chances of a word containing di. • Transitional probabilities quantify this. • The Transitional Probability of xy is: xy x

12. Transitional Probabilities • So for the word babupu, the transitional probability of babu is calculated as follows: Frequency of babu / Frequency of ba  1/2 = 0.5 Frequency of bupu / Frequency of bu  1/4 = 0.25 Overall transitional probability of the word babupu = (0.5+0.25) / 2 = 0.375

13. What’s the point? • Transitional probability was manipulated so that: • The transitional probability was high within a word, but low across a word boundary. This is what a word IS in real life.

14. High Transitional Probability High Transitional Probability Low Transitional Probability High Transitional Probability High Transitional Probability Low Transitional Probability High Transitional Probability High Transitional Probability ba bu pu bu pa da du ta ba

15. 300 tokens of each of the six words were randomly concatenated. • All word boundaries were removed • This left 4536 continuous syllables, which were read by a speech synthesizer. • Synthesizer produced a monotone of syllables at a rate of 216 syllables per minute.

16. Procedures • Subjects consisted of 24 undergraduate students. • Subjects were told to listen to ‘nonsense’ language. • Task is to figure out where words begin/end. • After 3 blocks of 7 minutes of exposure to the language, subjects were tested.

17. Test Procedure • Subjects heard two tri-syllabic strings, e.g., bu-pa-da and pi-da-bu Real word Not a real word Which sounds more like a word from this nonsense language? 36 trials in the test.

18. Results • Mean score correct for all subjects was 27.2, where chance is 18. t-test shows this to be statistically significantly different from chance. • Conclusion: adults are able to recognize what is a word and what is not a word based purely on statistical frequency.

19. Additional finding: • the three words with the most common syllables in them were easiest to recognize. • the three words with the least common syllables in them were hardest to recognize.

20. But can kids do this too? • Answer appears to be Yes. • Saffran et al. (1996) used essentially the same stimuli on 8 month old children • Used four strings of words instead of six. • Children were exposed for only 2 minutes (not 21 minutes)

21. Methodology • Head turning Procedure Child light speakers

22. Results • Children looked statistically longer at the speaker from which novel words were being produced. • Why is this? Why wouldn’t they look longer at the speaker from which familiar words are being produced?

23. Bottom Line • Children have the ability to track transitional probabilities of sounds on the basis of very little exposure. • This is therefore how words are parsed.

24. Tool against Nativism…? • This has recently been the most prolific weapon against the idea that children use innate knowledge to acquire language. • If children are using such sophisticated skills to segment words, why can’t they use similar (non-linguistic) skills to learn syntax?

25. But it isn’t so simple • Marcus et al. (1999) trained children on sentences of the following sort: • la – ta – la • ga – na – ga • da – ba – da • x – y – x

26. And tested them on: • wo – fe – wo • gi – tu – gi • po – zi – po Namely, words with: • new syllables, but • the same structure (x-y-x) And… • wo – fe – fe • gi – tu – tu • po – zi – zi Namely, words with: • new syllables, and • new structure (x-y-y)

27. Results • Children appear to recognize the difference between these sets of stimuli  Children are therefore tracking structure and not just simple statistics.

28. Questions to ask yourself: • Why would statistical tracking be useful to linguists? • As a tool to explain language acquisition. • Does statistical tracking explain how children acquire language? • No, only certain aspects of it. • What aspects of language can we track? So far, it appears only phonologically related things can be tracked like this (not meaning-related things).

29. Most Important Questions • Is this useful for ALL languages on Earth?  It appears that statistical tracking is only useful for auditory stimuli, not visual…ASL? • Are humans the only creatures that can do this? (I hope so, otherwise other animals should have language too…)  No. Vervet and Tamarin monkeys have been shown to have essentially the same abilities that humans do.

30. So what do we really know? • Kids have spectacular abilities to track statistics. • But so do adults (so why can’t adults learn languages as well as kids?) • But so do monkeys (so why can’t monkeys learn language as well as humans?) • This ability appears to be limited to statistics in auditory perception.