Speech Recognition lexicon DAY 19 – Oct 9, 2013

1. Speech Recognition lexiconDAY 19 – Oct 9, 2013 Brain & Language LING 4110-4890-5110-7960 NSCI 4110-4891-6110 Harry Howard Tulane University

2. Brain & Language, Harry Howard, Tulane University 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.

3. Brain & Language, Harry Howard, Tulane University Review

4. Brain & Language, Harry Howard, Tulane University Review

5. Brain & Language, Harry Howard, Tulane University The Speech Recognition lexicon Ingram §7

6. Brain & Language, Harry Howard, Tulane University Linguistic 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

7. Brain & Language, Harry Howard, Tulane University 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.

8. Brain & Language, Harry Howard, Tulane University 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?

9. Brain & Language, Harry Howard, Tulane University 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!

10. Brain & Language, Harry Howard, Tulane University An alternative: the TRACE II model

11. Brain & Language, Harry Howard, Tulane University 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.

12. Brain & Language, Harry Howard, Tulane University Simple recurrent networks • Read what Ingram says to get the general idea of what it is supposed to do.

13. Brain & Language, Harry Howard, Tulane University Modeling variability • We will go over it on Monday.

14. Brain & Language, Harry Howard, Tulane University NEXT TIME Q5. Finish Ingram §7 & start §8. ☞ Go over questions at end of chapter.