Responding to questions-in-overlap: on the ...

Responding to questions-in-overlap: on the relationship of phonetic and interactional-sequential design Marianna Kaimaki University of Cambridge [email protected]

John Local University of York [email protected]

PLEASE DON’T CITE OR CIRCULATE

1

Preliminary Results ‘The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.’ (Tukey 1986: 74)

1.1

Phonetic design and hearability: statistical modelling

In order to address the issue of the phonetic design of questions-in-overlap and the role it might play in determining whether they are treated as unproblematic or not, we now examine in detail relationships between phonetic features of questions-inoverlap with their immediate contexts1 . The contexts and relationships are pictured in Figure 1 by solid arrowed lines; dotted arrowed lines indicate comparisons we make for purposes of normalisation between talk-in-overlap and talk by the same speakers in the clear. We examine not only the direct relationship of questions to the talk they overlap (eg. difference between F0 mean/range of questioner’s in-overlap talk compared with that of the overlapped speaker) but also of the talk in overlap of both speakers to their prior talk. Previous work on the phonetics of overlapping talk by Kurtić and colleagues has indicated that local modifications to such talk may be important in explicating the workings of overlap. For instance, it is known that in designing competitive as opposed to non-competitive incomings in overlap, participants typically slow down their speech rate with respect to the tempo of their preceding talk (French and Local 1983; Kurtić 2011). Such local changes or adjustments in pitch, loudness or tempo between overlap and pre-overlap talk might have a role to play in distinguishing between questions-in-overlap which are treated as hearable or not.

1

As will be obvious in the sections that follow, we are heavily indebted to the novel work of Emina Kurtić whose PhD thesis makes an important contribution to quantification and model testing for phonetics-and-interaction. See Kurtić (2011).

Kaimaki & Local Turns-in-clear

Question-speaker

Overlapped-speaker

Questions-in-overlap Pre-overlap

Overlap

Pre-Question talk

Question-in-overlap

Pre-Overlapped talk

Questions-in-the-clear

Overlapped talk

Figure 1: Schematic representation of phonetic comparisons made in the analysis. (Adapted from Kurtiˇc, Brown & Wells, 2013).

Following Kurtić et al. (2013) we compare phonetic features (F0 mean, range intensity mean, range, speech rate) for each speaker’s questions-in-overlap to the same features in three contexts: the questioner-speaker’s norm (eg F0 mean of that speaker’s questions-in-the-clear), the question speaker’s pre-question talk and the overlapped talk. We also compare the overlapped speakers talk with their their norm (talk in the clear) and with their pre-overlapped talk. We compute z-scores for the phonetic variables for both speakers. For each case we also code information about the sequential positioning of the question and whether or not the question was treated as unproblematically hearable in overlap. Tables 23, 24 and 25 in Appendix E provide full details of the phonetic variables and comparisons that we make. Table 26 lists the sequential features of questions and overlapped talk. We employ two main statistical techniques to investigate the predictive strength of the phonetic variables: mixed effects logistic regression and a decision-tree learner paradigm (Quinlan 1993). 1.1.1

Candidate predictors of hearability

Our investigation attempts to be as open-minded as possible about how or in what combinations phonetic features might function to discriminate questions in the context of overlap. Nonetheless, informed by research in CA and on listening in adverse conditions, we predict that questions-in-overlap which are treated as unproblematically hearable will: 1. display a greater F0 range than the talk they overlap 2. display a greater mean Intensity than the talk they overlap 3. display a great Intensity range than the talk they overlap 2

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Questions-in-overlap

4. display relatively short durations of overlap 5. have a smaller percentage of the question in overlap than those which are treated as problematic We begin by constructing a series of mixed-effect models with ‘hearability’ as the response variable with gender and speaker as random effects. We evaluate model fit, importance of features and prediction accuracy using a variety of methods (including likelihood ratio tests, classification rates and receiving operating characteristics (ROC curve)). Table 1 gives the results for the candidate predictors: F0 range, mean intensity, overlap duration and percentage of question in overlap. Tables 3 - 6 report the results of the best logistic models for ensembles of F0 , Intensity and Duration separately and in combination as predictors. Table 7 reports the area under the ROC curve (ROC) and Somers’ Dxy rank correlation between the predicted probability of the occurrence of the outcome and the observed outcome.2 Taken together the results indicate that the predicted differences and ensembles/combinations of F0 , Intensity and Duration features fail to reveal any statistically significant relationship to hearability. In no case is model fit improved by including gender and/or speaker as random factors. Predictor

Estimate

Std. Error

z value

Pr(>|z|)

(Intercept) QF0RangeReO QIntMeanReO QIntRangeReO duroverlap percentoverlap

0.51333 -0.14462 -0.93388 -0.36482 0.08242 0.00315

0.92256 0.08853 0.64422 0.19677 0.85643 0.01220

0.556 -1.634 -1.450 -1.854 0.096 0.258

0.5779 0.1023 0.1472 0.0637 . 0.9233 0.7963

Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Table 1: Mixed-effects logistic regression model of candidate predictors of hearability.

These results indicate that phonetic features do not play a role in distinguishing questions in overlap which are hearable (and receive answers) and those that are not (and engender repair initiation). However interpretation is not straightforward as each of these models were fit after dealing with differing degrees of collinearity between features. The models in Tables 3 - 6 are fit by exclusion of features 2

ROC curves are a commonly used way to characterise the performance of binary classification and of assessing the discrimination of a fitted logistic model. The curve is generated by plotting the true positive classification rate against the false positive classification rate at various threshold settings. A value of 1 indicates perfect classification while a value of 0.5 indicates classification at random. Somers’ Dxy measure ranges between 0 (randomness) and 1 (perfect prediction).

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Model

ROC

Dxy

n

Missing

Table 1

0.68

0.35

94

0

Table 2: Somers2 results for model in Table 1.

Predictor

Estimate

Std. Error

z value

Pr(>|z|)

(Intercept) QF0MeanReOb4 QF0MeanReO QF0MeanReQb4 QF0RangeReOb4 OF0RangeReOb4 QF0RangeReO

0.89104 0.12784 -0.27502 0.04895 0.52639 -0.46848 -0.61971

0.74880 0.26594 0.35095 0.19345 0.40438 0.36579 0.38423

1.190 0.481 -0.784 0.253 1.302 -1.281 -1.613

0.234 0.631 0.433 0.800 0.193 0.200 0.107

Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Table 3: Mixed-effects logistic regression model of candidate F0 predictors of hearability.

Predictor

Estimate

(Intercept) 0.827467 QIntMeanReO 0.362918 QIntMeanReQb4 -0.658629 QIntRangeReClearQ 0.008292 QIntRangeReO -0.303229

Std. Error

z value

Pr(>|z|)

0.673217 0.852280 0.633888 0.265504 0.263394

1.229 0.426 -1.039 0.031 -1.151

0.219 0.670 0.299 0.975 0.250

Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Table 4: Mixed-effects logistic regression model of candidate Intensity predictors of hearability.

informed by examining the Variable Inflation Factor (Marquardt 1970) for all features. The VIF was computed using the vif and vifstep functions from the R package usdm).3 In light of this it is not clear that these negative results are robust.

3

‘The Variance Inflation Factor is based on the square of the multiple correlation coefficient resulting from regressing a predictor variable against all other predictor variables. If a variable has a strong linear relationship with at least one other variables, the correlation coefficient will be close to 1 and the VIF for that variable would be large’ (Naimi 2015).

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Predictor


Estimate

(Intercept) 0.025293 QODur -1.452696 QSRReO -0.193339 QSRReQb4 0.060134 OSRReOb4 -0.039559 Qsyllsover 0.344998 Qpercentoverlap 0.004644

Std. Error

z value

Pr(>|z|)

1.021555 1.781954 0.185486 0.141761 0.149411 0.316811 0.012623

0.025 -0.815 -1.042 0.424 -0.265 1.089 0.368

0.980 0.415 0.297 0.671 0.791 0.276 0.713

Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Table 5: Mixed-effects logistic regression model of candidate Duration/Speech Rate predictors of hearability.

Predictor

Estimate

Std. Error

z value

Pr(>|z|)

(Intercept) QF0MeanReClearQ OF0MeanReOb4 QF0MeanReQb4 QF0RangeReOb4 OF0RangeReOb4 QF0RangeReO QF0RangeReQb4 QSRReO QSRReQb4 OSRReOb4 QIntMeanReClearQ QIntMeanReO QIntMeanReOb4 QIntRangeReClearQ QIntRangeReOb4 OIntRangeReOb4 QIntRangeReQb4 QDur QODur

-1.438005 -0.111113 0.446190 -0.192427 0.243444 -0.406251 -0.606090 0.003567 -0.027166 0.011347 0.072054 2.993045 -0.235082 -2.128259 -0.079771 -0.394073 0.447968 0.156276 1.027481 -1.319403

2.523639 0.330246 0.319618 0.258507 0.382459 0.411360 0.416756 0.003295 0.150925 0.152838 0.142633 1.791378 1.083325 1.386430 0.442729 0.398392 0.348340 0.289478 0.961823 1.468028

-0.570 -0.336 1.396 -0.744 0.637 -0.988 -1.454 1.083 -0.180 0.074 0.505 1.671 -0.217 -1.535 -0.180 -0.989 1.286 0.540 1.068 -0.899

0.5688 0.7365 0.1627 0.4566 0.5244 0.3234 0.1459 0.2789 0.8572 0.9408 0.6134 0.0948 . 0.8282 0.1248 0.8570 0.3226 0.1984 0.5893 0.2854 0.3688

Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Table 6: Mixed-effects logistic regression combined model of candidate F0 , Intensity and Duration predictors of hearability.

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Model

ROC

Dxy

n

Missing

Table 3 (F0 )

0.6135

0.2271

94

0

Table 4 (Intensity)

0.6286

0.2573

94

0

Table 5 (Duration/Speech Rate)

0.6739

0.3479

94

0

Table 6 (Combined)

0.7437

0.4875

94

0

Table 7: Somers2 results for models in Tables 3 - 6.

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In order to test the validity of this finding and develop the analysis further we turned to a different statistical technique: decision-tree learning (Breiman et al. 1984; Quinlan 1993; Witten and Frank 2005). Decision-tree learning, a common machine learning technique, implements probabilistic classifiers. The technique has a number of strengths relevant to the present investigation: • interactions and nonlinear relationships between features do not affect tree/classification performance • performance is robust in the presence of skewed distributions, noisy data and outliers. • the output tree is easy to understand and interpret (Quinlan 1993: 9) Decision-tree learners are canonically employed in two modes: training and validation. In training mode the algorithm is presented with a set of features and a class-labelled training set (in our case F0 , Intensity and Duration/Speech Rate features with cases class-labelled as yes/no hearable). The algorithm repeatedly selects a single feature that, according to an information gain (entropy) criterion, yields the highest predictive value and best splits the training data into their target classification categories. In validation mode the predictive power of the tree model is evaluated against a test data set which consists of cases, not in the training set, with unknown class labels. So, for instance, if particular features or feature combinations distinguish the two kinds of question in the present corpus, the decision-tree model that makes use of them will successfully classify instances as hearable or not.

2

Decision-tree modelling

We trained decision-trees on our data using an R-Statistics implementation of Quinlan’s C4.8 decision-tree learner (Hornik et al. 2009; Quinlan 1993). Following standard practice we employed 10-fold stratified cross-validation to predict the error rate in evaluating the decision-trees.4 We followed Kurtić et al. (2013) and compared the performance of decision-trees trained on different features and feature combinations to the performance of a majority baseline classifier, which classifies all instances as the class that occurs most often in the data. For the 4

‘The data is divided randomly into 10 parts in which the class is represented in approximately the same proportions as in the full dataset. Each part is held out in turn and the learning scheme trained on the remaining nine-tenths; then its error rate is calculated on the holdout set. Thus the learning procedure is executed a total of 10 times on different training sets . . . Finally, the 10 error estimates are averaged to yield an overall error estimate.’ (Witten and Frank 2005: 150)

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present data the majority class is the hearable questions and the correctness of the majority baseline is 68.1%. The results reflect those of the logistic regression modelling – classification accuracy was poor and in the case of Duration/Speech Rate features and Allfeatures-combined significantly worse than the baseline classifier. Table 8 provides details and gives two additional measures of (weighted average) accuracy: area under the ROC curve and Matthews Correlation Coefficient.5 Phonetic features

Cases correctly classified

ROC Area

MCC

F0 all

63.7%

0.452

-0.124

Intensity all

61.7%

0.493

-0.006

Duration/SpeechRate all

59.5%*

0.386

-0.162

All-features-combined

54.2%*

0.415

-0.236

* statistically significant worse performance compared to the majority baseline classifier (68.1%) Table 8: Classification accuracy of the decision-tree models trained on the features in the first column. All significance values are reported as indicated by a two-tailed paired t-test, p< 0.05.

We take these results as a confirmation that the non-significant results found in the logistic regression modelling are robust and that phonetic features do not play a role in distinguishing the questions-in-overlap. This raises the possibility that which of the questions-in-overlap are treated as heard and unproblematic is simply a matter of chance. However, given the body of evidence in CA concerning the importance of interactional-sequential structure on the treatment of turns, a more plausible account is that there may be non-phonetic, interactional reasons why some of the questions-in-overlap are treated as problematic and as having hearability issues. We take up this possibility in section 3.

5

The MCC measure is standardly used as a robust performance measure on unbalanced data sets, which the present data set is: 1 represents perfect classification -1 complete misclassification.

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??ASIDE: INCLUDE? ILLUSTRATION OF RELATIVELY QUIETER Q NO PROBLEM, RELATIVELY LOUDER Q PROBLEM (1) En4093-660 1 2 3 4 5 6 7

A:

B: A:

erm if they say well . . . it’ll take us six months to get phone and all that you know .hhhh we’ll just have to wait but .hhh uh[m if we do have a ph]one number[does it take that long] (0.6) I don’t know how long it takes I’m just mocking the Dutch system

Question-in-overlap treated as unproblematically hearable even though it is quieter than talk it overlaps (Figure 2).

Intensity (dB)

100

30 uh

if

does

we

it

take

682.7

do

phone

have a

that

long 684

Time (s)

Figure 2: EN4093-660. Relative intensity of question-in-overlap (continuous line) and overlapped talk (dashed line) with corresponding word annotations.

(2) En4245-686 1 2 3 4 5 6 7 8

A:

B: A: → B: A:

yeah I’ve been smoking big time (.) it’s been a rough couple of months (0.5) yeah tch well- yeah [it’s been really busy] [the kid sister thing] (0.5) huh

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9 10

B: A:


the kid sister thing .hhhhh well no not really that

Question-in-overlap treated as having hearability problems but louder than talk it overlaps (Figure 3)

Intensity (dB)

100

30 it’s

been

the

kid

really

busy

sister

9.258

thing

10.49 Time (s)

Figure 3: EN4093-660. Relative intensity of question-in-overlap (continuous line) and overlapped talk (dashed line) with corresponding word annotations.

3

Interactional-sequential design and hearability

To test whether interactional-sequential features determine the status of questionsin-overlap as problematic or not, we trained decision-trees on the interactional categories of the Stivers and Enfield coding scheme and additional categories relating to the sequential placement of the talk under investigation (Appendix E, Table 26). The best model trained solely on interactional features achieved 84.85% success in classification. This is significantly better than the majority baseline classifier but still some way short of what might be desired (11/30 of those questions actually treated as problematic and requiring repair were wrongly classified). Only two interactional-sequential features had significant impact on classification. The best model depended crucially on a feature relating to the answer to the question (whether the (eventual) answer was indirect or direct) and a feature of the beginning of the question turn (whether or not the question began with a receipt of the prior turn). Figure 4 shows the relevant tree. 10

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1 answerdir

n

y

2 QReceiptatbeg

n

Node 4 (n = 3)

1

Node 5 (n = 71)

1

0.8

0.8

0.6

0.6

0.6

0.4

0.4

0.4

0.2

0.2

0.2

0

0

y

0.8

y

n y

y 1

n

n Node 3 (n = 20)

0

Figure 4: Best decision tree model trained on sequential-interactional features.

The tree shows that the best predictor is whether on not the question receives a direct (as opposed to an indirect answer). A inspection of the data-coding reveals that there is a noticeable difference between the two sets of responses — when answers are given following redone questions after NTRI’s there are proportionately more indirect responses (63%, N = 19) than in the set treated as unproblematically hearable where answers are given without repair initiation (6.25%, N = 4). The presence of a receipt of the prior turn at the beginning of the question further segregates the data. While there are not many cases (N=4), none occur where questions are treated as having hearability problems and are subsequently redone. Fragments 3 to 6 give a sense of how these two kinds of answer play out in our data. In fragment 3 there is a direct answer to an unproblematic questionin-overlap and fragment 4 illustrates an eventual direct answer to a questionin-overlap treated as problematic where repair is sought. Fragment 5 illustrates an indirect answer in to an unproblematic question-in-overlap while fragment 6 illustrates an indirect to a problematic question-in-overlap. Note also that fragment 5 shows a receipt of the prior turn at the beginning of the question-in-overlap turn. (3) En0638-381 1 2 3 4 5 6

A: B: A: A:

it’s a month to month lease so I’m sure oh really (.) yes (0.4) [but I d-]

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7 8 9

B:

→ A:


[how much] (0.4) it’s two ninety five a month

(4) En4065-1220 1 2 3 4 5 6 7 8

B: B: A: B: A:

→ B:

suddenly I get an email from somebody you know .hhh a[re you ] are[you’re on email] (0.8) what you’re on email (0.6) yeah

(5) En4065-1220 1 2 3 4 5

A:

B:

→ A:

...go like .hhh oh I’ve been working in the schools in Pittsburg doing blah blah blah blah b[lah so you should just let me come in and take over he]re [aha .hh well what kind of stuff do you want to do] I don’t know that’s the problem

(6) En6479-258 1 2 3 4 5 6 7 8 9 10 11 12 13

A: B: A: B: A: B: A: B: A:

→ B:

.hh[h hah hah hah hah] [but things are always intere]sting hih heh [hih [hih (0.3) .h[hhh ] [nuh- ] [.hhh] [oh it] souds like i[t [yeah it [r e a l l y is] [do you have a ta]n .hhh what (0.9) do you have a tan (0.3) a tan well I haven’t bee- .hh well I just got back

Both the nature of the answers and the presence of a receipt at the beginning of the question turn tell us something about how the question turns relate to and are fitted to prior talk. Having a receipt of the prior turn at the beginning of a question serves to tie that turn to its immediately preceding sequential context; an indirect response indicates that there is potentially an interactional-pragmatic problem with the question in relation to the prior talk (Curl 2005; Heinemann 2009; Walker et al. 2011). [THIS SECTION TO BE ELABORATED] We will demonstrate through close interactional analysis of the sequences in question that the ‘hearability problems’ arise not from the design of the 12

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question-turn itself but from the relationship of this turn to the prior sequence. Questions-in-overlap treated as having hearability problems are either interactionally-pragmatically inapposite and/or they are not sequentially fitted to the ongoing talk.

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