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Cours 04

De
48 pages
Speech production• What happens when we speak?• One idea: a spreading activation model (Dell 1986)Speech production• How could this model be tested?– Test predictions regarding time course of production– Look at ways the model can fail and compare to humans• add noise = speech errors• damage network = patients with impairmentsSpeech errors: why?• Looking at how the model goes wrong can tell us how it is put together – What are the units of speech production? – How are units interconnected? – What is the domain of speech planning? Speech errors: how?• Problem: speech errors are very rare; we can’t easily observe them in the lab • Two solutions: – Collect overheard errors over a long time • problem: perceptual bias: are you equally likely to notice all kinds of errors? – Increase chances of making an error in an experimental situation • rapid pronunciation of difficult sequences (cf. “tongue twisters”) • Example: repeat quickly hes nev pem gek (Goldrick & Larson 2008) Types of speech error• Errors are not random; only some possible error types occurSome tendencies• Similar units tend to interact:– phonemes with other phonemes, words with other words– phonemes in same syllable position – words of same grammatical category • Errors tend to respect the rules of the language – proue tordue troue pordue more likely than– clou tordu tlou corduPhoneme similarity effect• Similarity effect extends to phonetic properties of phonemes – ...
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Speech production
What happens when we speak?
One idea: a spreading activation model
(Dell1 986)
Speech production
How could this model be tested?
–Test predictions regarding time course of production
– Look at ways the model can fail and compare to humans
• add noise = speech errors
• damage network = patients with impairments
Speech errors:why?
Looking at how the model goes wrong can tell us how it is put together
– What are the units of speech production?
– How are units interconnected?
– What is the domain of speech planning?
Speech errors:how?
Problem: speech errors are very rare; we cant easily observe them in the lab Two solutions: – Collect overheard errors over a long time problem: perceptual bias: are you equally likely to notice all kinds of errors? – Increase chances of making an error in an experimental situation • rapid pronunciation of difficult sequences (cf. “tongue twisters”) Example: repeat quickly hes nev pem gek
(oGlrdick& Larson 2008) 
Types of speech error
Errors are not random; only some possible error types occur
Some tendencies
Similar units tend to interact: – phonemes with other phonemes, words with other words – phonemes in same syllable position – words of same grammatical category
Errors tend to respect the rules of the language –proue torduetroue porduemore likely than –clou tordutlou cordu
Phoneme similarity effect
Similarity effect extends to phonetic properties of phonemes
–batbadmore likely thanbatbang • [t] and [d] differ only in voicing • [t] and [ng] differ in multiple features: nasality, place, voicing
Lexical bias Errors tend to result in words more than non-words
–
ex. errors with two phonemes being swapped
expected % of resulting words: 45
observed % of resulting words: 61
“With thiswing, I theered”
Modifying
the model
Inner speech
How is inner speech different from audible speech? Some possibilities: –meprnoivhUeisd: processing is exactly the same, except articulators aren’t moved –durSpmi-ecafehsirevo: activation at phonological level is weakened or absent (because no articulation) –Deep-impoverished: activation at lexical level is weakened or absent (because lexical information is not represented in short-term memory and hence in the production-perception loop)
Theory
Unimpoverished
Surface-impoverished
Deep-impoverished
Predictions
Phoneme similarity
yes
no
yes
Lexical bias
yes
yes
no