Speech Perception DAY 18 – Oct 9, 2013
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Transcript of Speech Perception DAY 18 – Oct 9, 2013
SPEECH PERCEPTIONDAY 18 – OCT 9, 2013
Brain & LanguageLING 4110-4890-5110-7960NSCI 4110-4891-6110Harry HowardTulane University
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Course organization• The syllabus, these slides and my recordings are
available at http://www.tulane.edu/~howard/LING4110/.• If you want to learn more about EEG and neurolinguistics,
you are welcome to participate in my lab. This is also a good way to get started on an honor's thesis.
• The grades are posted to Blackboard.
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REVIEW
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Linguistic model, Fig. 2.1 p. 37
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Discourse model
SyntaxSentence prosody
MorphologyWord prosody
Segmental phonologyperception
Acoustic phonetics Feature extraction
Segmental phonologyproduction
Articulatory phonetics Speech motor control
INPUT
SEMANTICS
Sentence level
Word level
SPEECH PERCEPTIONIngram §6
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A baby’s linguistic experience• Are babies sensitive to phonological distinctions in their
mothers’ speech?• Yes. • How can this be?
• They must be able to hear in the womb.
• In their first few months, babies prefer …• their caretaker’s voice;• phonological distinctions in their linguistic environment;• speech rhythms in their linguistic environment.
• Summary: before children utter their first words, their perceptual system is being attuned to their linguistic environment.
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Head turning• Baby attends to toy and ignores repetitions of a stimulus until it changes:
• a – a – a – i
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Perceptual magnet effectsPrototype /i/ (P) and
non-prototype (NP) vowelsResult: reduced discrimination near
prototype
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Discussion of PME• Animals can’t do this. Hooray!• It depends on exposure to a language – it emerges only
after 6 months.
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THE SPEECH RECOGNITION LEXICONIngram §7
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Linguistic model, Fig. 2.1 p. 37
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Discourse model
SyntaxSentence prosody
MorphologyWord prosody
Segmental phonologyperception
Acoustic phonetics Feature extraction
Segmental phonologyproduction
Articulatory phonetics Speech motor control
INPUT
SEMANTICS
Sentence level
Word level
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Storing on a hard disk• How does a computer store files on its hard drive?• By writing them in
sequence or where ever there is space.
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Retrieving from a hard disk• How does a computer find files on its hard drive (say, when you search for one by its name)?• It searches for it in
sequence or randomly.• How long does it take?
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How would this work for lexical retrieval?
• Ingram’s example• The phoneme detector department detects /k/.• A comparator starts looking for all the files that begin with /k/,
perhaps ordered in terms of frequency.• The phoneme detector department detects /æ/.• The comparator rejects the files that don’t begin with /kæ/ and starts
searching the remaining files, perhaps ordered in terms of frequency.• “It is an open bet whether the word cat would be retrieved before or
after the detection of /t/.” (p. 143)• Problems
• Other factors influence speed of retrieval, such as whether the target word has been seen recently.
• Adding such factors to a serial search model tends to make it slow down!
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An alternative: the TRACE II model
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Observations• TRACE implements parallel computation, rather than serial
or sequential computation.• It is both bottom-up (driven by data) and top-down (driven
by expectations).• Bottom up
• The successive winnowing of a set of cohorts is modeled by decaying activation of competitors as more information is gathered.
• Top down• Word frequency is modeled by lowering the threshold of activation of more
frequent word units, so they need less activation.• The phoneme restoration effect is modeled by the word units supplying the
missing activation of a phoneme unit.• [kæØ] can be heard as ‘cat’.
• The Ganong effect is modeled in the same way.• [kæ<sʃ>] can be heard as ‘Cass’ or ‘cash’ in the proper context.
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Simple recurrent networks• Read what Ingram says to get the general idea of what it
is supposed to do.
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Modeling variability• We will go over it on Monday.
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NEXT TIMEFinish Ingram §7.
☞ Go over questions at end of chapter.
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