The information profile of Spanish word systems: I. The case of speech addressed to a child. II. An analysis by featu

1. The information profile of Spanish word systems:I. The case of speech addressed to a child. II. An analysis by feature. Monica Tamariz Richard Shillcock monica@ling.ed.ac.uk rcs@cogsci.ed.ac.uk

2. Overview • Use information profiles of word systems (corpus, lexicons). • More realistic representations of speech generate flatter profiles. • Flatter profiles reflect a more efficient use of the representational space.

3. Assumptions • Phonology plays a part in the organization of the mental lexicon. • For maximal efficiency, information should be spread as evenly as possible over the representational space.

4. Distribution of information over the representational space

5. Entropy • Concept of entropy from information theory (Shannon, 1948). • Measure of the uncertainty or information H = -  (pi · log pi) • Redundancy R = 1 - H

6. Data sets • Speech corpus: 707,000 word tokens. • Speech lexicon: 42,000 word types. • Dictionary lexicon: 28,000 headwords.

7. Transcriptions • Citation (30 phonemes) • Fast-speech (50 phonemes and allophones)

8. Fast-speech transcription • Glides • Approximant B D G • Consonant assimilation e.g. [] [z] [dental n], [dental l]

9. Transcriptions

10. The Information Profile

11. The Information profile Slope (-m) Mean level of entropy (Hrel)

12. (Levelling effect of realistic representations) “Processes that make the representation of words more accurate will flatten the information profiles” The LERR principle

13. The effect of the transcription:Information profile slopes • Fast speech has flatter profiles (as in other languages) • Longer words have flatter profiles

14. The effect of the transcription: level of entropy. • More entropy in the citation transcription. • Fast speech is more redundant and thus, more predictable.

15. The Speech Lexicon: Information profile slopes. • Speech Lexicon: the active mental lexicon represented in the brain. • The speech lexicon has flatter profiles.

16. The Speech Lexicon:Level of entropy • Speech lexicon: low redundancy levels. • Level varies little across word lengths. • Support for Butterworth ‘Full Listing Hypothesis’.

17. Corpus vs. Lexicon: Information profile slopes • Corpus: representation over time. • Lexicon: representation over space. • The lexicon yields flatter profiles.

18. Corpus vs. Lexicon: Level of entropy. • The lexicon generates higher entropy levels.

19. Discussion • Fast-speech rules and a ‘Full List’ mental lexicon flatten the information profile. • In the speech lexicon, the main constraint is efficiency of storage. • In the corpus, other constraints - such as lexical segmentation - interact with the optimization of communication.

20. Conclusion • This simple analysis of the information profile of word systems is a useful tool that can provide insights into the validity of psycholinguistic theories.

22. I. Speech addressed to a child • Corpus “Maria” of speech addressed to a child aged 1-4 years. • Limited vocabulary. 41,000 word tokens : 3,900 word types. • High frequency of diminutives (-ito, -ita).

23. Speech addressed to a child: Information profile slopes • “Child” speech yields flatter slopes. • “Child” lexicon slopes are steeper.

24. Speech addressed to a child: Level of entropy • “Child” speech shows more phonological redundancy.

25. The lexicon addressed to a child • The steep slope of the lexicon suggests it contains cues to word boundaries. • Infants are sensitive to the probabilistic phonotactics of the language. • Storage is not a constraint but word segmentation is, to “teach” children about the probabilistic phonotactics of the language.

27. II. Analysis by feature • Features instead of phonemes • Consonants only • Two analyses (a) By manner of articulation (b) By place of articulation

28. Analysis by feature:Information profiles • Fast-speech transcriptions of the corpus

29. Analysis by feature: Information profile slopes • Manner vs. place analysis • Citation vs. fast-speech • Corpus vs. lexicon

30. Analysis by feature:Level of entropy • Phoneme vs. Features • Manner vs. Place of articulation

31. Analysis by feature: Discussion • Fast-speech rules flatten feature analysis slopes. • Manner of articulation is most efficient over time - for communication (in the corpus). • Place of articulation is not so efficient, but could have a role in word boundary recognition. • Phoneme representation is most efficient over space - for storage (in the lexicon).

33. III. Speech addressed to a child: Analysis by feature: slopes • Manner vs. place analysis • Citation vs. fast-speech • Corpus vs. lexicon

34. Adult • Manner vs. place analysis • Citation vs. fast-speech • Corpus vs. lexicon

35. Child • Manner vs. place analysis • Citation vs. fast-speech • Corpus vs. lexicon

36. Speech addressed to a child: Analysis by feature: level of H • Adult vs. child • Phoneme vs. Features • Manner vs. Place of articulation

37. Adult

38. Child

39. Speech addressed to a child: Analysis by feature: level of H • Adult vs. child • Phoneme vs. Features • Manner vs. Place of articulation

40. Discussion • Fast-speech flattens feature analysis slopes. • Manner of articulation is most efficient over time - for communication (in the corpus). • Place of articulation is even less efficient than in adult (Steeper slope and higher H) - role in word boundary recognition. • Phoneme representation not as efficient over space - for storage (in the lexicon) as adult speech.

41. Discussion • Speech addressed to a child makes a use of features such that: • A feature representation is relatively more efficient than a phoneme representation of the mental lexicon. • Place of articulation has an enhanced role in encoding word segmentation cues.