We are extracting NPs of the form (DT NN) from Switch Board in NXT and exploring the effects of predictability (among other things) of the DT and NN on speech rate.
Corpus
All NPs that consist of a determiner (DT) and a noun (NN) were extracted from Switch Board in NXT FootNote(http://groups.inf.ed.ac.uk/switchboard/index.html) which did not occur in an unfinished utterance (i.e. "...the bicy-"), did not contain any coded disfluencies (i.e. "...the uh bicycle...") and the NP was not a direct projection of another NP. This constituted 16313 items. The following data was also extracted for both the DT and NN: spoken duration, number of syllables (citation form), number of phonemes (citation form), number of syllables in the current speech windowFootNote(not quite sure where the details of this is located), frequency (in Switch Board), probability (unigram), log forward and backward probability (bigram), forward and backward joint frequency (bigram), givenness, animacyFootNote(following http://npcorpus.bu.edu/documentation/index.html the distinction is human > organizations and animals > other), speaker ID, speaker gender and speaker age. Further, the following information was extracted: the word immediately before the DT and the word following the NN. The following information was imported in: phonological typicality of the NNFootNote(provided by Thomas Farmer), the stressed and unstressed log frequency weighted neighborhood densityFootNote(from iphod v2 http://www.iphod.com/) of the DT and NN and the average stressed and unstressed log frequency weighted biphone probability of the DT and NNFootNote(from iphod v2 http://www.iphod.com/). The following information was calculated from the above: phones per second for the DT and NN, syllables per second for the DT and NN, syllables per second for the speech window for the DT and NN (adjusted, i.e. (syllables in speech window minus syllables in word)/(seconds in speech window minus seconds in word)).
Exclusions
Cases with missing values or non-sensical values (such as zero syllables) were removed for the following measures: number of phonemes (DT & NN), number of syllables (DT & NN), duration (DT & NN), syllables per second (DT & NN), phonemes per second (DT & NN), speech window syllables per second (DT & NN), both neighborhood density estimates and average biphone probabilities. This resulted in a loss of about 7.76% of data (15047 cases left). In addition, to remove outliers, the following variables were log10 transformed, normalized, and values above and bellow a value of 2.5 were removed: NN & DT frequency, NN & DT probability, NN & DT duration, NN & DT syllables per second, NN & DT phonemes per second and NN & DT speech window syllables per second. This resulted in a loss of 17.8% of data (12364 cases left).
Control Variables
After the above exclusions, the the following measures were calculated for both DTs and NNs: mean duration by word (i.e. mean duration for all 'the's), mean log10 duration, mean phonemes per second, mean log phonemes per second, mean syllables per second and mean log syllables per second. Analysis of control variables were assessed for the following DVs (both DT & NN): log duration, log phonemes per second, log syllables per second. Speaker ID was included as a random effect in all the analyses. The following results list which factors were significant alone and which factors were significant after accounting for others. In the case of DTs, they were all monosyllabic and thus, syllable count was not included.
DT
Log Duration
mean log duration was significant over and above all other factors
log phonemes was significant, but not after controlling for mean log duration
log speech window syllables per second was significant over and above all other factors
speaker age and speaker gender were not significant
Phonemes per Second
mean log phonemes per second was significant over and above all other factors
log phonemes was significant, but not after controlling for mean log phonemes per second
log speech window syllables per second was significant over and above all other factors
speaker age was not significant
speaker gender was significant above and beyond all other factors
Syllables per Second
mean log syllables per second was significant over and above all other factors
log phonemes was significant, but not after controlling for mean log syllables per second
log speech window syllables per second was significant over and above all other factors
speaker age and speaker gender were not significant
NN
Log Duration
mean log duration was significant over and above all other factors
log phonemes was significant, but not after controlling for mean log duration
log syllables was significant, but not after controlling for mean log duration
log speech window syllables per second was significant over and above all other factors
speaker age was not significant
speaker gender was marginally significant (including over log speech window syllables per second), but not after controlling for log syllables, log phonemes, or mean log duration
Phonemes per Second
mean log phonemes per second was significant over and above all other factors
log phonemes was significant, but not after controlling for mean log phonemes per second
log syllables was significant, but not after controlling for mean log phonemes per second
log speech window syllables per second was significant over and above all other factors
speaker age was not significant
speaker gender was significant above and beyond all other factors
Syllables per Second
mean log syllables per second was significant over and above all other factors
log phonemes was significant, but not after controlling for mean log syllables per second
log syllables was significant, but not after controlling for mean log syllables per second
log speech window syllables per second was significant over and above all other factors
speaker age and speaker gender were not significant
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