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Segmenting Nonsense

Segmenting Nonsense. Sanders, Newport & Neville (2002). Ricardo Tabone LIN 7912. Background. Behavioural studies  adults segment continuous speech using several segmentation cues Problem: these studies cannot distinguish between fast segmentation and slower linguistic processing

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Segmenting Nonsense

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  1. Segmenting Nonsense Sanders, Newport & Neville (2002) Ricardo Tabone LIN 7912

  2. Background • Behavioural studies  adults segment continuous speech using several segmentation cues • Problem: these studies cannot distinguish between fast segmentation and slower linguistic processing • Speech segmentation has been studied in different groups of speakers (e.g. young infants, bilingual adults, etc) through different tasks • There is a need for an experimental task that can be employed with all groups: Recording ERPs!

  3. Background (cont) • In continuous speech, initial syllables elicit larger negativity (N100) than medial syllables. (Sanders & Neville, in press) • Initial and medial syllables were controlled for loudness, length and other acoustic characteristics. • But…..

  4. Research Question • Do N100 word-onset effects index speech segmentation rather than acoustic characteristics pertaining to word boundaries? • In other words, is speech segmentation affected by lexical processing of speech sounds?

  5. Background (recap) • In continuous speech, initial syllables elicit larger negativity (N100) than medial syllables. • Initial and medial syllables were controlled for loudness, length and other acoustic characteristics. • But….. • Interesting: Behavioural tests  exposure to a continuous stream of nonsense words allows listeners to learn to distinguish between nonsense words and part-word items

  6. Experimental Design • Pre-test  Subjects listened to 36 pairs of 3-syllable Nonsense Words (NWs) and indicate which of the two items seemed more familiar. • Each pair consisted of one of the 6 NWs that would be used later and one part-word items composed of the last syllable of a NW + the first 2 syllables from another word • First Test  Subjects listened to a continuous stream of the 6 NWs (babupu, bupada, dutaba, patubi, pidabu, tutibu), repeated randomly 200 times each • The words were generated by text-to-speech synthesis and were sequenced without pauses: • Babupubupadababupudutabapatubipidabututibu

  7. Experimental Design • Pre-test  Subjects listened to 36 pairs of 3-syllable Nonsense Words (NWs) and indicate which of the two items seemed more familiar. • Each pair consisted of one of the 6 NWs that would be used later and one part-word items composed of the last syllable of a NW + the first 2 syllables from another word • First Test  Subjects listened to a continuous stream of the 6 NWs (babupu, bupada, dutaba, patubi, pidabu, tutibu), repeated randomly 200 times each • The words were generated by text-to-speech synthesis and were sequenced without pauses: • Babupubupadababupudutabapatubipidabututibu

  8. Experimental Design (cont) • ERPs were recorded during the first 14-minute exposure. • Second Test Determined whether or not subjected had learned to recognize words due to exposure alone • Next, trainingSubjects listened to the 6 NWs, separated by 500ms, for 10 minutes, and separated by 100ms for an extra 10 minutes. • Speakers were asked repeated the word and were presented with the text version on the screen. • Third Test  Assessed which words subjects learned • Fouth Test  ERPs were recorded during an extra 14-minute exposure to the same babupubupadababu…. • Firth Test  Assessed which words subjects learned

  9. Experimental Design (cont) • ERPs were recorded during the first 14-minute exposure. • Second Test Determined whether or not subjected had learned to recognize words due to exposure alone • Next, trainingSubjects listened to the 6 NWs, separated by 500ms, for 10 minutes, and separated by 100ms for an extra 10 minutes. • Speakers were asked repeated the word and were presented with the text version on the screen. • Third Test  Assessed which words subjects learned • Fouth Test  ERPs were recorded during an extra 14-minute exposure to the same babupubupadababu…. • Firth Test  Assessed which words subjects learned

  10. Participants • 18 participants • Right-handed • Monolingual English speakers

  11. Results (Behavioural) • Second Test  Subjects performed at ~50% • exposure alone isn’t enough. • Third Test  After training, participants performed at 79.5%. Bingo! • Fifth Test  Performance was measured again after another 14-minute exposure. Nothing changed (79.2%) • No new words learned, no old words forgotten.

  12. Results (Behavioural) • High correlation between individual performance on post-training tests and the difference in N100 amplitude before and after training

  13. ERPs Analysis • Divided the 18 participants into two groups: • 9 High learners (M=55.1%)  (M=90.7%) • 9 Low learners (M=52.2%)  (M=67.9%) • High Learners showed a significant effect of training on N100 amplitude, specially on medial and midline electrodes

  14. High Learners

  15. ERPs Analysis • Divided the 18 participants into two groups: • 9 Low learners (M=55.1%)  (M=90.7%) • 9 High learners (M=52.2%)  (M=67.9%) • High Learners showed a significant effect of training on N100 amplitude, specially on medial and midline electrodes • Low Learners did not show a significant N100 effect

  16. Low Learners

  17. ERPs Analysis • Divided the 18 participants into two groups: • 9 Low learners (M=55.1%)  (M=90.7%) • 9 High learners (M=52.2%)  (M=67.9%) • High Learners showed a significant effect of training on N100 amplitude, specially on medial and midline electrodes • Low Learners did not show a significant N100 effect • All subjects displayed a N400 effect • It is possible that this effect might influence medial and final syllables, since syllables last between 100ms to 300ms

  18. High Learners

  19. Low Learners

  20. Discussion • The N100 effect is similar to the one observed in processing English (Sanders & Neville, in press) • Artificial Language learners (McCandliss, Posner & Givón, 1997) also display the same N400 effects while learning new words • Japanese bilinguals also displayed a N400 effect and no N100 effect while listening to English (Sanders & Neville, in press)

  21. Conclusion • The results indicate than N100 effects cannot be solely explained on the basis of acoustic differences between initial and medial sounds • Listeners who are better at segmenting speech show earlier segmentation effects • Word-onsets effects are similar even when segmentation cues are very different • (e.g. NWs vs Native words) • N100 represents an automatic process of segmentation, whereas N400 indicates a slower, lexically-orientated process of segmentation.

  22. THEEND

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