Durational reduction in English (and Polish) words shows a contextual frequency effect

# Durational reduction in English (and Polish) words shows a contextual frequency effect
## APAP 2019
### Kamil Kaźmierski, WA at AMU in Poznań
### June 21st, 2019

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# Overall frequency effect

- Higher lexical frequency → Shorter duration (e.g. Jurafsky et al. 2001)

+ Perhaps caused by speakers: frequency of use causes articulatory reduction

+ Perhaps moderated by listeners' expectations: highly frequent forms don't have to be pronounced carefully
    
- Such reduction could result from online computation, performed on abstract phonological representations (cf. Levelt et al. 1999)

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# Contextual frequency effect on variation

<table class="table table-striped" style="width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> Language </th>
   <th style="text-align:left;"> Effect </th>
   <th style="text-align:left;"> Source </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> English </td>
   <td style="text-align:left;"> Prevocalic word-final .ipa[/t/] glottalized more in words typically followed by consonants </td>
   <td style="text-align:left;"> Eddington &amp; Channer (2010) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> English </td>
   <td style="text-align:left;"> Word-final .ipa[/t,d/] deletion more likely in words typically followed by consonants </td>
   <td style="text-align:left;"> Raymond et al. (2016) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> English </td>
   <td style="text-align:left;"> Unstressed ING more likely to be .ipa[/ɪn/] in words frequently occurring in .ipa[/ɪn/] favoring contexts </td>
   <td style="text-align:left;"> Forrest (2017) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Spanish </td>
   <td style="text-align:left;"> Latin .ipa[/fV-/] words frequently occurring after word-final non-high vowels likely to be <hv-> .ipa[/ØV-/] in MSS </hv->
</td>
   <td style="text-align:left;"> Brown &amp; Raymond (2012) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> New Mexican Spanish </td>
   <td style="text-align:left;"> Word-initial .ipa[/s/] more likely to be reduced (.ipa[[s]] › .ipa[[h]] › .ipa[[Ø]]) in words often preceded by word-final non-high vowels </td>
   <td style="text-align:left;"> Raymond &amp; Brown (2012) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> English </td>
   <td style="text-align:left;"> Words typically predictable reduce in duration more </td>
   <td style="text-align:left;"> Seyfarth (2014) </td>
  </tr>
</tbody>
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# Predictability and Informativity

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# Predictability

- Transitional/conditional probability (cf. Jurafsky et al. 2001): `$$P(w_i|w_{i-1})=\frac{C(w_{i-1}w_i)}{C(w_{i-1})}$$`

- One problem: bigrams with 0 occurrences in the corpus

- A solution: add **1** to each bigram count

- A better solution: Modified Kneser-Ney smoothing (cf. Chen & Goodman 1998)  ([*r-cmscu*](https://github.com/jasonkdavis/r-cmscu) R package)

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# High vs. low predictability

$$ $$

**Lower** (< 0.001) predictability of *home* given *nice*

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$$ $$

**Higher** (0.412) predictability of *home* given *fortress-like*

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# Higher predictability → More reduction

$$ $$

**Lower** predictability of *home* given *nice*

→ **Less** reduction in *home*
]

$$ $$

**Higher** predictability of *home* given *fortress-like*

→ **More** reduction in *home*
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# Higher predictability → More reduction

**Right**-context predictability: How likely is this word, given the word the speaker is **about to say** next?

**Higher** predictability of *home* given *wrecker*

→ **More** reduction in *home*
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**Lower** predictability *home* given *course*

→ **Less** reduction in *home*
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# Two different predictability "profiles"

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# Informativity :: How unpredictable from its context is this word on average?

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# Calculating Informativity

???

Take Kneser-Ney smoothed probability of a particular word token in a particular context

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# Calculating Informativity

???

Seyfarth used bans (base 10 logarithm) as is apparently usual, I used natural logarithm (R's default) in nsp, corrected it to bans for gpsc

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# Calculating Informativity

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# Calculating Informativity

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# Calculating Informativity

.footnote[
Steven T. Piantadosi, Harry Tily, & Edward Gibson 2011. "Word lengths are optimized for efficient communicationTitle", *Proceedings of the National Academy of Sciences* 108(9), 3526-3529.
]

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# Informativity vs. Frequency

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# Predictability vs. Informativity

Word frequently occurs in **low-predictability** contexts → **high** informativity

Word frequently occurs in **high-predictability** contexts → **low** informativity

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# Target study: Seyfarth (2014)

- Durational reduction in Buckeye (Pitt et al. 2007) and Switchboard-1 Release 2 (Calhoun et al. 2009; Godfrey & Holliman 1997)

- Predictability and informativity estimateted from Fisher Part 2 corpus (Cieri et al. 2005)

- Findings:

- Higher left-context and right-context predictability → More reduction

- Higher right-context (both corpora) or left-context (Switchboard) informativity  → Less reduction

- Implications:

- Predictability and reduction: could be an online effect

- Informativity and reduction: suggests storage of reduced forms

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# Research questions

## **RQ1**: Do the results of Seyfarth (2014) replicate on another English dataset?

## **RQ2**: Do these effects generalize to Polish?

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# Method

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# Source of English data

Nationwide Speech Project Corpus

[(Clopper & Pisoni 2006)](https://www.ncbi.nlm.nih.gov/pubmed/21423815)

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# Model architecture (*N* = 7,158)

- Response: 
   + `Word duration`

--
- Predictors of theoretical interest: 
   + `Predictability given previous`, `Informativity given previous`, `Predictability given following`, `Informativity given following`

--
- "Control" predictors:
   + `Part of speech`, `Orthographic length`, `No. of syllables`, `Dialect`, `Average speaking rate`, `Rate deviation`

--
- Random terms:
   + `(1|Word)`, `(1 + Informativity given following + Informativity given previous | Speaker)`

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# Results

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# Predictability given following

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# Informativity given following

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# Source of Polish data (*N* = 10,588)

[**wa.amu.edu.pl/korpuswlkp**](http://wa.amu.edu.pl/korpuswlkp)

(Kaźmierski, Kul & Zydorowicz in press)

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# Predictability given previous

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# Predictability given following

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# Informativity given following

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# Summary of results

## **RQ1**: Do the results of Seyfarth (2014) replicate on another English dataset?

## ✓ Yes

### Differences: predictability given previous; informativity given previous (Switchboard)

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# Summary of results

## **RQ2**: Do these effects generalize to Polish?

## ✓ Yes

### local predictability given both previous and following word, as well as informativity given following word are significant predictors of word duration in Polish words

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# Conclusions

## → The effect of local predictability is stronger for right-hand context than left-hand context

## → On top of local predictability, both English and Polish show the effect of informativity

## → The latter effect suggests phonological storage of reduced forms

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# Thank you!

## Right-context informativity influences word durations in English and in Polish

[kamil.kazmierski@wa.amu.edu.pl](!mailto:kamil.kazmierski@wa.amu.edu.pl)

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# Model summary: English

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# Model summary: Polish

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