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
• 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!
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…..
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?
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
Experimental Design
• Pre-test  Subjects listened to 36 pairs of 3syllable 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
Experimental Design
• Pre-test  Subjects listened to 36 pairs of 3syllable 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
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 14minute exposure to the same babupubupadababu….
• Firth Test  Assessed which words subjects learned
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 14minute exposure to the same babupubupadababu….
• Firth Test  Assessed which words subjects learned
Participants
• 18 participants
• Right-handed
• Monolingual English speakers
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.
Results
(Behavioural)
• High correlation between individual performance
on post-training tests and the difference in N100
amplitude before and after training
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
High Learners
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
Low Learners
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
High Learners
Low Learners
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)
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.
THE
END