Speech Perception DAY 18 – Oct 9, 2013

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Speech Perception DAY 18 – Oct 9, 2013. Brain & Language LING 4110-4890-5110-7960 NSCI 4110-4891-6110 Harry Howard Tulane University. Course organization. The syllabus, these slides and my recordings are available at http://www.tulane.edu/~howard/LING4110/ . - PowerPoint PPT Presentation

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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

10/07/13 Brain & Language, Harry Howard, Tulane University

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

10/07/13 Brain & Language, Harry Howard, Tulane University

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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