1 / 18

An Adaptive, Dynamical Model of Linguistic Rhythm

An Adaptive, Dynamical Model of Linguistic Rhythm. Sean McLennan GLM 040312. Underlying Intuitions. Somewhere between the signal and low level speech recognition, linguistic time is imposed upon real time. Linguistic time is more relevant to speech recognition than real time.

bschrader
Download Presentation

An Adaptive, Dynamical Model of Linguistic Rhythm

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. An Adaptive, Dynamical Model of Linguistic Rhythm Sean McLennan GLM 040312

  2. Underlying Intuitions • Somewhere between the signal and low level speech recognition, linguistic time is imposed upon real time. • Linguistic time is more relevant to speech recognition than real time. • Not all segments are created equal - certain points / intervals in the speech stream are more important for recognition than others. GLM - Sean McLennan - 040312

  3. What “Rhythm” Is and Is Not Rhythm - historically based primarily on the perception that different languages are temporally organized differently Three recognized rhythmic types: stress-timed (English), syllable-timed (French), and mora-timed (Japanese) Rhythm implies underlying isochrony which turns out to be absent (ex. Dauer, 1983) GLM - Sean McLennan - 040312

  4. Recent Views of Rhythm Ramus and colleagues: • examined three factors: %V ΔV ΔC • %V = proportion of vocalic intervals in the signal • ΔV = variation of length of vocalic intervals • ΔC = variation of length of consonantal intervals GLM - Sean McLennan - 040312

  5. Recent Views of Rhythm GLM - Sean McLennan - 040312

  6. Recent Views of Rhythm GLM - Sean McLennan - 040312

  7. Recent Views of Rhythm GLM - Sean McLennan - 040312

  8. Rhythm and Segmentation Cutler and Colleagues • study the question of how rhythm type impacts on the segmentation of words from the speech stream • implication being that a naïve listener (i.e. an infant) uses rhythm as a bootstrap for early stages of acquisition GLM - Sean McLennan - 040312

  9. Rhythm and Segmentation Syllable Effect: • French speakers spot “ba-” in balance faster than in balcon • French speakers spot “bal-” in balcon faster than in balance • rigorously reproduced, even on non-French words • “stubbornly” absent in English GLM - Sean McLennan - 040312

  10. Rhythm and Segmentation Stress Effect • Native English speakers find “mint” faster in mintesh than in mintayve • Native English speakers find “mint” slower in “mintayf” than in “mintef” and “thin” in thintayf or thintef. • In missegmentations - tend to insert before a stressed syllable (in vests) or delete before a weak syllable (bird in) GLM - Sean McLennan - 040312

  11. Rhythm and Segmentation Mora Effect • Native Japanese speakers find “ta-” in tanishi faster than in tanshi • Native Japanese speakers find “tan-” faster in tanshi than in tanishi. • Native Japanese speakers can find “uni” in gyanuni and gyaouni but fail to find it in gyabuni. • Native English speakers have no problem with the Japanese task • Native French speakers show the same cross-over effect with the Japanese task as in French and English GLM - Sean McLennan - 040312

  12. The Proposed Model • hopefully a bridge between Cutler et al and Ramus et al - why should %V ΔV ΔC impact on segmentation? • can a naïve adaptive model responsive to %V ΔV and ΔC produce behavior consistent with segmentation based on rhythm-type? GLM - Sean McLennan - 040312

  13. The Proposed Model • %V ΔV and ΔC need two points to be consistently tracked: vocalic onsets and offsets GLM - Sean McLennan - 040312

  14. The Proposed Model • Use these spikes to drive two adaptive oscillators (habituating neurons?) • Unlikely to entrain but will make predictions GLM - Sean McLennan - 040312

  15. The Proposed Model • The accuracy of prediction will be a measure of ΔC and ΔV • Difference in the period will be a measure of %V GLM - Sean McLennan - 040312

  16. The Proposed Model • ΔC ΔV and %V in turn determine the size of an “attentional window” • the attentional window is a metaphor for stimulus decay • The smaller ΔC and ΔV and closer %V is to 50%, the more periodic the rhythm, the narrower the window can be • The larger ΔC and ΔV and more divergent %V is from 50%, the less periodic the rhythm, the wider the window must be GLM - Sean McLennan - 040312

  17. The Proposed Model • Attentional window size (hopefully) would correlate with rhythm type and would predict different types of segmentation / recognition GLM - Sean McLennan - 040312

  18. The Proposed Model Predictions, questions, and other benefits: • consistent with the correlation between rhythmic type and consonant cluster complexity • consistent with ambisyllabicity • perhaps attractor states predict categorical differences • suggests manner in which to manipulate tasks to force effects • single language-independent mechanism GLM - Sean McLennan - 040312

More Related