speech recognition lexicon day 19 oct 9 2013
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Speech Recognition lexicon DAY 19 – 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/ .

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speech recognition lexicon day 19 oct 9 2013

Speech Recognition lexiconDAY 19 – Oct 9, 2013

Brain & Language

LING 4110-4890-5110-7960

NSCI 4110-4891-6110

Harry Howard

Tulane University

course organization
Brain & Language, Harry Howard, Tulane UniversityCourse 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.
linguistic model fig 2 1 p 37
Brain & Language, Harry Howard, Tulane UniversityLinguistic model, Fig. 2.1 p. 37

Discourse model

Semantics

Sentence level

Syntax

Sentence prosody

Word level

Morphology

Word prosody

Segmental phonology

perception

Segmental phonology

production

Acoustic phonetics Feature extraction

Articulatory phonetics Speech motor control

INPUT

storing on a hard disk
Brain & Language, Harry Howard, Tulane UniversityStoring 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.
retrieving from a hard disk
Brain & Language, Harry Howard, Tulane UniversityRetrieving 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?
how would this work for lexical retrieval
Brain & Language, Harry Howard, Tulane UniversityHow 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!
observations
Brain & Language, Harry Howard, Tulane UniversityObservations
  • 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.
simple recurrent networks
Brain & Language, Harry Howard, Tulane UniversitySimple recurrent networks
  • Read what Ingram says to get the general idea of what it is supposed to do.
next time
Brain & Language, Harry Howard, Tulane UniversityNEXT TIME

Q5. Finish Ingram §7 & start §8.

☞ Go over questions at end of chapter.

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