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Talker versus dialect effects on speech intelligibility: a symmetrical study
Daniel R. McCloy* and Richard A. Wright
University of Washington, Seattle WA
Pamela E. Souza
Northwestern University and Knowles Hearing Center, Evanston IL
Running head: Talker vs. dialect intelligibility
*Author to whom correspondence should be addressed. Electronic mail: drm[email protected].
Postal mail: Daniel R. McCloy, University of Washington, Institute for Learning & Brain
Sciences, Box 357988, Seattle WA, 98195-7988.
Acknowledgments: This research was supported by the National Institutes of Health, National
Institute on Deafness and Other Communication Disorders [grant R01-DC006014]. The authors
are grateful to Namita Gehani, Jennifer Haywood and August McGrath for help in stimulus
preparation, and to members of the UW Phonetics laboratory and anonymous referees for helpful
suggestions on earlier drafts of this paper. Portions of the research described here were
previously presented at the 164th Meeting of the Acoustical Society of America in Kansas City,
MO, and published in Proceedings on Meetings in Acoustics.
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ABSTRACT
This study investigates the relative effects of talker-specific variation and dialect-based variation
on speech intelligibility. Listeners from two dialects of American English performed speech-in-
noise tasks with sentences spoken by talkers of each dialect. An initial statistical model showed
no significant effects for either talker or listener dialect group, and no interaction. However, a
mixed-
revealed a subtle effect of talker dialect once the various acoustic dimensions were accounted
for. Results are discussed in relation to other recent studies of cross-dialect intelligibility.
Keywords: speech intelligibility, dialect variation, speech perception in noise, acoustics, mixed
models
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INTRODUCTION
This study investigates the relative effects of talker-specific variation and dialect-based variation
on speech intelligibility. Unlike some previous studies of cross-dialect speech perception, it
involves a fully-crossed design (all dialects studied are represented among both the talkers and
the listeners) and explicit control for talker-level contributions to intelligibility that could
otherwise contribute to sample bias (making one dialect appear more or less intelligible due to
dialect-irrelevant aspect
to quantify differences in talker-intrinsic intelligibility that emerge in the study.
BACKGROUND
It is well established that the pronunciation of an utterance can vary significantly from talker to
talker even when the content of the utterance and the communicative conditions are fixed (Black,
1957; Bond & Moore, 1994; Hood & Poole, 1980). Idiosyncratic pronunciation characteristics
that underlie this variation are sometimes referred to as indexical traits, for which two broad
classes of explanation have been proposed. Scholars following Abercrombie (1967) attribute
indexical traits to a
context exposure (in addition to the idiosyncratic physiological and kinematic characteristics of
each ). These are typically treated as relatively constant influences on a
, although their manifestation in the speech signal is not necessa
the sense of being observable at all points or time scales. In contrast, Silverstein (2003) and
others view indexical traits as manifestations of the sociolinguistic, discourse, and pragmatic
signaling behaviors that a particular talker engages in for a particular communicative task. These
behaviors can be seen as varying from utterance to utterance as the communicative context
changes.
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Within these two broad sources of between-talker variation, dialect in particular has been
much scrutinized, especially as it relates to speech intelligibility. For example, when listeners
hear speech in their own dialect it is usually more intelligible than when it is in an unfamiliar
dialect (Labov & Ash, 1997; Mason, 1946). Listeners perform increasingly poorly in vowel
identification tasks when synthetic-vowel stimuli are modeled on dialects that are progressively
divergent from their own (Wright & Souza, 2012). Similarly, Oder and colleagues (2013)
demonstrated that vowel identification error patterns are consistent with vowel acoustic
differences between dialects using naturally produced vowels. Listeners also perform worse on a
variety of language-processing tasks when stimuli are drawn from an unfamiliar dialect than
when stimuli are in a familiar dialect, and worse yet when the stimuli are in a non-native accent
(Adank, Evans, Stuart-Smith, & Scott, 2009). However, listeners do show adaptation to
unfamiliar dialects or accents through exposure or training (Baese-Berk, Bradlow, & Wright,
2013; Bent & Holt, 2013; Bradlow & Bent, 2008; Kraljic, Brennan, & Samuel, 2008); this
suggests that intelligibility differences between dialects are at least in part, if not predominantly,
a reflection of listener experience.
Nonetheless, a few studies have suggested that some dialects are intrinsically more
intelligible than others. This is analogous to the many studies showing that talker-intrinsic
pronunciation traits can lead to talker-intrinsic intelligibility differences (e.g., Bradlow, Torretta,
& Pisoni, 1996; Payton, Uchanski, & Braida, 1994; Picheny, Durlach, & Braida, 1985). Two
recent studies of dialect-level differences in intrinsic intelligibility will be reviewed here:
(2008) study of dialect intelligibility and classification in noise, and
(2014) study of dialect intelligibility in multitalker backgrounds.
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Based on two earlier studies of dialect similarity and classification (Clopper, Levi, &
Pisoni, 2006; Clopper & Pisoni, 2007), Clopper and Bradlow (2008) examined the intelligibility
of four American English dialects: Mid-Atlantic, Northern, Southern, and General American (the
last of which is a meta-dialect comprising speakers from the Midlands, Western U.S., and New
England). Clopper and Bradlow found differences in intelligibility between the dialects increased
in more challenging noise levels, with General American talkers being most intelligible and Mid-
Atlantic talkers being least intelligible across a range of noise levels regardless of listener dialect.
They interpret their results as indicating that General American is the most intrinsically
intelligible dialect to their listeners, who were drawn from three groups: Northern, General
American, and mobile (listeners who had lived in two or more dialect areas before the age of
18). Somewhat surprisingly, the General American talkers were more intelligible than the
Northern talkers even to Northern listeners. This could be taken (somewhat implausibly) to
indicate that Northern listeners were somehow more familiar with General American speech than
they were with their own dialect, or (more plausibly) that the difference in intelligibility of the
dialects (which favored General American) outweighed the difference in familiarity (which
presumably favored Northern speech).
One limitation of the Clopper and Bradlow study is that the listener-
fully symmetrical. That is, while the Northern and General American dialects were represented
in both the listener and talker groups, there were no Southern or Mid-Atlantic listeners and no
mobile talkers. Therefore, it is difficult to definitively attribute intelligibility differences to
intrinsic dialect traits rather than listener familiarity with those dialects. Indeed, part of Clopper
and nation for the intelligibility advantage of General American is that listeners
are likely to have encountered it and be at least somewhat familiar with it. A second limitation is
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that all listeners heard a practice set of 12 sentences from a different set of General American
talkers before hearing the test stimuli. This could have resulted in a dialect-familiarization
advantage for the stimulus-set of General American talkers. Of most relevance to the current
study, there was very little effort to control for or measure intrinsic intelligibility of the talkers.
While they did find that vowel space area and vowel space dispersion were not significantly
different across dialects, there were intelligibility advantages for gender groups (differing by
dialect) that were not predictable from the vowel space measures, indicating that there is at least
some other dimension contributing to gender-based intelligibility effects. The study had a
moderate number of talkers from each dialect group (three male and three female) but their
statistical analysis does not include talker as random effect to see if there might be individual
pronunciation differences within the set of regional talkers that may be unintentionally
contributing to the apparent intelligibility advantage of the General American talkers.
In a more recent study, Jacewicz and Fox (2014) found that Southern talkers were more
intelligible than General American talkers when the listener group was General American, and
that the Southern-talker intelligibility advantage increased as listening conditions deteriorated.
They also found that, in more difficult listening conditions, Southern talkers were more
effectively masked by Southern-talker babble than they were by General American-talker babble,
but that General American talkers were masked equally well by both dialects. The first result
(2008) findings (discussed above) showing that General
American was most intelligible to all listener groups that they studied (which included General
American listeners). Like the Clopper and Bradlow study, the Jacewicz and Fox study was also
asymmetrical, with listeners only from the General American region.
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As Jacewicz and Fox point out, the tasks in the two studies are not identical: the sentence
material and the masker noise both differed between their study and Clopper and
study. They also note that their General American speakers are drawn from a narrower
geographic area than Clopper and , so dialect homogeneity
may play a role in explaining the difference in findings. Importantly, Jacewicz and Fox note that
their Southern talkers produced speech with a pitch pattern that differed from the General
American talkers despite producing speech at a comparable tempo. They attribute the differences
in pitch to dialect-specific differences, but, like the Clopper and Bradlow study, Jacewicz and
Fox do not conduct a det
contribute to the intelligibility differences, nor do they use talker as a random variable in their
statistical design. Therefore, it remains to be seen if their apparent dialect-intrinsic intelligibility
effect favoring Southern talkers is a genuine dialect effect rather than an effect of the individuals
who made up their samples of talkers.
In light of the questions raised by the two studies just discussed, the current study probes
the relative contribution of dialect and individual pronunciation variability in a symmetrical two-
dialect sample. It includes a detailed acoustic analysis of talker traits that are thought to
contribute to intelligibility. The study involves talkers and listeners from two dialect regions with
fairly narrow definitions: Pacific Northwestern (PN) English (a subregion of the General
American variety used in both studies) and Northern Cities (NC) English (a subregion of the
Northern region in Clopper an).
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METHODS
Stimulus Set
Stimuli were sentences drawn from the PN/NC corpus (McCloy et al., 2013), a subset of the
(Rothauser et al., 1969) read by five males and five females from
each of two dialect regions: the Pacific Northwest (PN) and the Northern Cities (NC). The PN
region was defined as Washington, Oregon, and Idaho, and is a sub-
defined in both Clopper et al (2005) and Labov et al (2006, p. 137). The NC region was defined
following Labov et al (2006, pp. 121124) as the sub-
the low-back distinction between /a/ and // in both production and perception. It is a sub-region
(Clopper et al., 2006,
2005), who largely follow Labov et al (2006).
From the full set of 720 Harvard sentences, 200 were selected based on avoidance of contrast of
-mounted close-talking
microphone (Shure SM10A) to ensure consistent and maximal signal-to-noise ratio (SNR) of
the raw recordings, and the 200-sentence block was recorded three times per talker. Talkers were
coached to speak in a relaxed manner with no special effort or emphasis. Talkers exhibiting list
intonation across sentences were notified of this behavior and coached to produce falling
declarative intonation on every sentence. Three trained phoneticians chose the best instance of
each sentence from each talker for inclusion in the corpus, determined by two criteria: lack of
mic overloading or clipping, and absence of hesitations and disfluencies. All stimuli were hand-
trimmed (with careful attention to low-, padded
with 50 ms of silence at the beginning and end, and RMS normalized. From the 200 sentences
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recorded, 20 were reserved for task familiarization, yielding a final corpus of 3600 stimuli (5
talkers per group × 2 genders × 2 dialect regions × 180 sentences).
Perception Task
Each listener first heard 20 unique training sentences (one from each talker) presented at three
SNRs (6 sentences in clear and 7 at each of +2 dB and +6 dB SNR levels). The training
sentences were all distinct from the test sentences. Each listener then heard all 180 test sentences
in the corpus (each containing five keywords), drawn in equal numbers from the 20 talkers.
Sentences were presented in quiet and in two levels of background noise (+6 dB and +2 dB
SNR). The masker in the noise conditions was gaussian noise filtered to match the long-term
spectral average of the corpus. To ensure target audibility, the level of the speech was held
constant at 68 dB SPL (dB RMS in a 6 cc coupler) and different levels of masker noise were
digitally added to the speech to achieve the desired SNRs. The combined signal was presented in
a sound-insulated booth over closed-back supra-aural headphones (Sennheiser HD 251 II).
Listeners were instructed to repeat each sentence they heard, to give partial answers when they
only heard some words, and to guess when they were unsure. Trials were scored 05 on
keywords correct during the task. An audio recording was made of listener responses, and
scoring uncertainties were resolved offline by a second researcher. The 900 keywords were all
content words, with the following exceptions: 7 instances of pronouns (it, you, your, she, her, he,
him) and 25 instances of prepositions (across, against, beside, into, from, off, under, when, with,
without). 81% of the keywords were monosyllabic, the remaining 171 were disyllabic; no
sentence had more than one disyllabic keyword. Talker-sentence-SNR assignments were random
and unique for each listener, with the following constraints: (a) each listener heard 9 sentences
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from each talker; (b) each listener heard each talker at all three SNRs (3 sentences at each SNR);
(c) each listener heard each sentence only once.
Participants
Listeners were drawn from the same two dialect regions used to create the corpus: the Pacific
Northwest (PN) and Northern Cities (NC). By chance, all PN listeners were natives of
Washington state; NC listeners were natives of northern Ohio, northern Indiana, northern
Illinois, and Michigan (see Figure 1). All listeners were required to have lived in-region for ages
518, and to have not lived more than 5 years total outside their region. The mean age of the
listener group was 20.5 years for the PN listeners and 24.5 years for the NC listeners. All
listeners had bilaterally normal hearing, defined as pure-tone thresholds of 20 dB HL or better at
octave intervals from 250 Hz to 8 kHz (American National Standards Institute, 2004). Fifteen PN
listeners and thirteen NC listeners participated in the perception task.
Figure 1. Hometown locations of all 28 listeners.
Acoustic measurements
In order to characterize difference between talkers, a variety of acoustic measures of the corpus
were performed. Measures related to intensity, fundamental frequency (
0
) and several
characteristics of the F2 × F1 vowel space were obtained. Speech rate (syllables per second) was
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also calculated for each talker as the sum of the number of syllables in each sentence divided by
the sum of the durations of each sentence.
Vowel space measures Vowel space characteristics for each of the talkers were calculated from
the corpus materials based on hand-measurements of the first and second formants of 1100
vowel tokens (2 dialects × 10 talkers × 5 tokens per vowel for the 11 vowels
/i e æ a o u /; for PN talkers the vowel /
of the NC ta
inventory). Measured vowels were drawn from lexically stressed syllables in keywords in
positions throughout the sentence, with a preference for vowels with obstruent flanking
consonants to avoid coloring by adjacent nasals, rhotics, or laterals. Selection of vowels to
measure was balanced across vowel category, such that there were equivalent numbers of tokens
from keywords early, middle, and late in the sentence for each vowel type. Vowel boundaries
were marked following the methods of Peterson and Lehiste (1960): vowel onset was marked at
the release burst of a preceding plosive or at the start of periodicity when preceded by a fricative,
and vowel offset was marked at the cessation of periodicity. The hand-measured formant values
were taken from the 50% point of each vowel and converted to a perceptual scale using the bark
transform prior to statistical analysis.
Five measures of the vowel space were calculated from the formant data: mean Euclidean
distance from the center of the vowel space (Bradlow et al., 1996), area of the polygon defined
by F1 and F2 means for each vowel (Bradlow et al., 1996; Neel, 2008), area of the convex hull
encompassing all measured vowel tokens (McCloy, 2013), total repulsive force of the vowel
system (Liljencrants & Lindblom, 1972; Wright, 2004), and mean vowel cluster size (see Figure
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2).
1
Calculation of the area of the vowel polygon differs from previous studies in being based on
a large number of vowel phonemes (all vowels measured excluding //), in contrast to the /i o a/
triangle used by Bradlow and colleagues (1996), or the /i æ u/ quadrilateral used by Neel
(2008). The measurement of the area of the convex hull encompassing all vowel tokens is based
on the idea that a polygon based on F1 and F2 means for each vowel contains information about
a range of reduced and unreduced forms of each phoneme (and thus likely
prosodic habits to some degree), whereas a convex hull is a representation of the vowel space
based on most extreme unreduced pronunciations, and thus might abstract away from
individual differences in prosody (see McCloy, 2013, Chapter 5 for discussion).
Repulsive force of the vowel system) was calculated as
the sum of inverse squared distances between all pairs of vowel tokens not belonging to the same
phoneme, as in Equation 1 (where /i/ and /j/ represent the phonemic categories of the vowel
tokens being compared, and r is the Euclidean distance formula). This measures the degree to
which neighboring vowel phonemes in a system encroach on one another, with higher values of
repulsive force corresponding to greater degrees of phoneme overlap or encroachment. The
calculation seen here differs from both Liljencrants and Lindblom (1972) and Wright (2004) in
calculating force based on individual vowel tokens rather than mean values for each vowel.
1
It is noteworthy that we chose not to include measures of F1 and F2 range in our models, since both have
previously been reported to be significant predictors of intelligibility (Bradlow, Torretta, & Pisoni, 1996; Hazan &
Markham, 2004). When we included these measures in our statistical model, the model failed to converge (possibly
because F1 and F2 range are highly correlated with other measures of vowel space size, or possibly due to
insufficient statistical power for the large number of predictors included). We chose to omit these rather than other
measures because they are rather coarse measures of vowel space size that can easily be influenced by dialectal
variation, depending on the vowels measured. For example, Hazan and Markham (2004) measured F2 range based
only on tokens of /i/ and /u/, which could be strongly influenced by dialectal or gender differences in /u/-fronting (a
known feature of PN speech, cf. Reed, 1952; Ward, 2003, Chapter 4).
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Equation 1. Formula used to calculate repulsive force of the vowel space. /i/ and /j/ represent vowel phoneme categories,
and
is the Euclidean distance formula
.
Finally, mean vowel cluster size was calculated for each talker as the mean of the areas of the
95% confidence ellipses for each vowel category (based on bivariate normal density contours).
Low values of cluster size are associated with low degrees of within-category variation and
therefore (we predict) a more predictable perceptual target and higher intelligibility. We are not
aware of any previous studies of intelligibility that make use of this measure.
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Figure 2. Illustration of acoustic measures of the vowel space used in the statistical models. (a) Mean Euclidean distance
from center. (b) Area of the polygon formed by vowel means. (c) Repulsive force (color) and area of convex hull (shape).
(d) Mean cluster size.
Intensity- and pitch-related measures Because stimuli were RMS normalized, mean intensity
ends of utterances, or conversely to maintain a more consistent level across all the keywords in
the sentence. First, mean-subtracted intensity values were calculated using a gaussian moving
window with 50 ms bandwidth and 13.3 ms step size (i.e., a window corresponding to a 60 Hz
pitch floor and step size corresponding to 0.25 × effective window length). From these intensity
values, the rate of change of intensity was calculated at each point by subtracting the intensity
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value at the previous time point and dividing by the step size. Intensity velocity was defined as
the mean of these rate-of-
defined as the mean of the absolute value of the rate of change of intensity, as a measure of how
dramatic the rises and falls in intensity were across each sentence, irrespective of overall
intensity downtrend.
For measures of vocal pitch,
0
tracks were automatically extracted using Praat (Boersma
& Weenink, 2013) and a random subset of 15 of the 180 sentences were selected for hand-
correction. This yielded a total of 300
0
tracks for data analysis (15 per talker × 20 talkers).
From those 15 sentences the absolute and average
0
range magnitudes were calculated for each
talker,
2
as well as the mean rate of change in
0
absolute value of
rate of change in
0
). Like the intensity measures, pitch velocity indexes
overall sentence-level changes in pitch, while pitch dynamicity indexes how dramatic the rises
and falls in
0
were across each sentence, irrespective of overall
0
downtrend.
Dialect-related measurement issues Acoustic measures of the vowel space across talkers from
the PN and NC regions involves some dialect-related complexities due to the presence of the
low-back split preserved in the speech of NC talkers, and the corresponding merger of /a/ and //
to a single phoneme /This affects the calculation of vowel-space-related
measures that reflect the relationships among vowels in F2 × F1 space, such as polygonal vowel
space area, mean cluster size, and repulsive force. To address this issue, measures of polygonal
vowel space area, mean cluster size, and repulsive force were calculated based on the dialect
2
The choice to use mean size of pitch range in addition to absolute pitch range was motivated by the fact that a
given sentence may be uttered in a fairly monotone fashion even by a talker that has a large overall pitch range.
typical range across utterances is more indicative of their linguistic use of pitch than
their maximal range. Ideally, pitch range would be a stimulus-level predictor rather than a talker-level aggregate, but
reliable measures of pitch range for all 3600 stimuli was not possible given the need for hand correction (stemming
primarily from the difficulty of automatic pulse detection and pitch tracking algorithms in dealing with creaky
voicing).
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region of the talker, and z-score normalized within dialect (rather than across all talkers) prior to
statistical modeling.
relative to their within-dialect peers, thereby decoupling those predictors from other dimensions
of speech that might be dialect-linked and therefore might co-vary with a vowel space measure
that co-varied with talker dialect.
Data Analysis
Listener scores for keywords correct were modeled using mixed-effects regression with the lme4
package (Bates, Maechler, Bolker, & Walker, 2013) in R (R Development Core Team, 2013). As
can be seen in Figure 3, the clear and +6 dB SNR conditions showed ceiling effects, and
consequently were excluded from further analysis.
Figure 3. Mean keywords correct for the three SNR conditions, two talker groups, and two listener groups. Error bars
show one standard error. Ceiling effects are evident in the clear and +6 dB conditions.
Although during the experimental session the sentences were scored 0-5 on keywords correct,
scores were reduced to binary (1 = all keywords correct) prior to statistical analysis and were
modeled using a logistic link function. This was done for two reasons. First, it is inappropriate to
model a discretized 05 keyword score as continuous (because the data are restricted to six
discrete values and bounded between zero and five, violating the model assumption that the
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dependent variable is continuous and unbounded). Second, in this case modeling perception at
the level of individual keywords is less desirable given that our acoustic predictors are all
sentence- or talker-level measures. Another alternative, averaging across sentences within talker-
listener pairs to better approximate a continuous outcome, was rejected because it did not allow
modeling by-sentence random variation.
3
As mentioned above, acoustic measures related to the internal structure of the vowel
space (polygonal area defined by formant frequency means, mean cluster size, and repulsive
force) were z-score normalized within each talker dialect group prior to statistical modeling. The
other acoustic measures (mean distance from center of the vowel space, and measures related to
0
, and speech rate) are not known to systematically differ between these two dialects
in ways that would affect their calculation, so those measures were zscore normalized across all
talkers. A binary factor of talker gender was also included. The five acoustic measures of the
vowel space were analyzed for collinearity by computing the condition index of the predictor
The kappa value was 8.9, indicating mild-to-moderate
collinearity (kappa < 10 is a commonly used criterion for predictor inclusion).
RESULTS
The initial statistical model set out to test whether there was a (possibly asymmetrical) effect of
cross-dialect listening on talker intelligibility. Model results are shown in Table 1; there is no
significant interaction between talker dialect region and listener dialect region, nor are there
significant main effects for talker or listener dialect region separately. Not surprisingly, the
random effects show relatively high estimates of variance for sentence and talker (suggesting
3
Although there is arguably an important difference in listener performance between 4 keywords correct and no
keywords correct that is lost when converting scores to binary, this distinction may be less important with low-
recover from surrounding context, so that reporting 4 words correctly does not necessarily correspond to a listener
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that sentences varied in their difficulty and talkers varied in their intelligibility), and a relatively
low estimated variance for listener (suggesting that by and large, listeners were equally good at
the task). In fact, variation in intrinsic intelligibility of the talkers spanned a wide range, with the
least intelligible talker averaging only 2.6 keywords correct per sentence in the hardest noise
condition, and the most intelligible averaging 4.7 (see Figure 4).
Table 1. Summary of mixed-effects regression model testing the interaction between talker and listener dialects.
S.E. = standard error; s
2
= estimated variance.
Fixed effects (N = 1680; log-
Random effects
Predictor
Coefficient
S.E.
Wald Z
p
Group
s
2
Intercept
0.4759
0.3067
1.551
0.1208
Sentence
0.8575
Talker from PN
0.6502
0.3972
1.637
0.1016
Talker
0.6500
Listener from PN
0.4043
0.2152
1.879
0.0603
Listener
0.1408
TalkerPN:ListenerPN
0.2626
0.2294
1.145
0.2523
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Figure 4. Dotchart of mean intelligibility by talker in the +2 dB SNR condition. Dramatic differences across the groups
are evident in the horizontal spread within each group.
Accounting for talker variation using acoustic predictors
Given that there were no significant differences between the dialect groups overall, we ran a
second statistical model that included a variety of acoustic predictors, to see if a more subtle
dialect effect would emerge in a model that controlled for various acoustic dimensions of speech
known to impact intelligibility. The results of that statistical model are summarized in Table 2.
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Table 2. Summary of mixed-effects regression model testing the interaction of talker and listener dialects while
controlling for several acoustic predictors. S.E. = standard error; s
2
= estimated variance.
Fixed effects (N = 1680; log-
Random effects
Predictor
Coefficient
S.E.
Wald
Z
p
Group
s
2
Intercept
1.45545
0.40325
3.609
0.000307
Sentence
0. 8868
Talker from PN
2.09153
0.47977
4.359
0.0000130
Talker
0.00001
Listener from PN
0.40793
0.21681
1.881
0.059906
Listener
0.1394
Talk.PN:List.PN
0.26921
0.23273
1.157
0.247380
Talk. gender: male
1.13436
0.42235
2.686
0.007235
Speech rate
0.09063
0.19922
0.455
0.649167
Mean dist. center
0.36363
0.28063
1.296
0.195058
Repulsive force
0.53792
0.09024
5.961
2.51×10
9
Mean v. clust. size
0.80192
0.30576
2.623
0.008723
Convex hull area
1.86465
0.60866
3.064
0.002187
V. means polygon
0.53241
0.22967
2.318
0.020442
Overall pitch rng.
1.00874
0.26968
3.740
0.000184
Mean pitch range
2.99947
0.66325
4.522
6.12×10
6
Pitch velocity
2.37561
0.52072
4.562
5.06×10
6
Pitch dynamicity
1.55075
0.36468
4.252
0.0000211
Intensity velocity
0.43184
0.10204
4.232
0.0000231
Intensity dynam.
0.49245
0.11678
4.217
0.0000248
Examining the random effects, we see that the estimated variance across sentences and across
listeners has not changed substantially from the previous model, but the estimated variance
across talkers is dramatically smaller in the second model. This result is expected, since many of
for by the acoustic predictors, rendering the random effect for talker virtually unnecessary.
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More interestingly, the fixed effect for talker dialect has reversed direction: in the first model, a
talker being from PN predicted lower intelligibility (i.e., the model coefficient was negative,
though not significantly so), whereas in the model that controls for several acoustic predictors, a
talker being from PN predicts higher intelligibility (i.e., the model coefficient is positive).
Vowel space measures Among the acoustic predictors related to the vowel space, we see mostly
expected results: talkers are predicted to be more intelligible if they have larger convex hulls
encompassing their vowel space, smaller mean vowel cluster sizes, and lower repulsive force
(i.e., less phonemic crowding). Unexpectedly, we see a negative correlation between
intelligibility and the size of the vowel polygon defined by F1 and F2 means. One possible
explanation for this finding is that a polygon based on vowel means contains information about a
range of reduced and unreduced forms of each phoneme. Given that in our data vowel tokens
were all drawn from lexically stressed syllables in positions throughout the sentence, to the
extent that there is any reduction present, it is likely to be prosodically driven rather than
phonologically driven. As such, a large polygon might indicate that a talker is less effective at
making use of the vowel space to differentiate prominent from non-prominent words, which
might explain the negative correlation between intelligibility and the area of the polygon in this
data.
Pitch-related measures Among the acoustic predictors related to pitch, we see mostly expected
results: talkers are predicted to be more intelligible if they have a larger overall pitch range,
larger average pitch range magnitude across sentences, and higher mean pitch velocity (i.e., less
downdrift). An unexpected finding is that higher intelligibility is correlated with lower pitch
dynamicity, implying that larger pitch excursions to mark important words is actually
detrimental to intelligibility. However, another possibility is that the measure of pitch dynamicity
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conflates mid-sentence pitch excursions with sentence-final drops into creaky voicing, which is
likely to reduce intelligibility due to the concomitant drop in intensity during creaky phonation
(Gordon & Ladefoged, 2001; cf. McCloy, 2013, pp. 8386). In other words, the negative
correlation between pitch dynamicity and intelligibility may be dominated by a single, large,
utterance-final pitch excursion, masking the effect on intelligibility of earlier, smaller,
prominence-marking pitch excursions. A thorough investigation of this hypothesis would require
a more detailed measurement and transcription of pitch excursions than can be performed
automatically, so for this study we must remain agnostic regarding the role of pitch dynamicity
in intelligibility. Recall also that pitch measures were based on a random selection of 15 of the
180 stimulus sentences, and although we believe this to be a sufficient number to estimate the
-related findings
with some skepticism.
Intensity-related measures In this model, higher intelligibility is predicted by higher values of
mean intensity dynamicity, and by lower (i.e., more negative) values of mean intensity velocity.
The correlation with mean intensity dynamicity was expected, in that higher intensity dynamicity
was hypothesized to correspond with more consistent use of intensity to mark prominence (and
correspondingly to produce non-prominent words with reduced intensity). However, it is
possible that this measure would be less effective in predicting intelligibility in a study where
intelligibility were scored across all words in a sentence, rather than based on linguistically
prominent keywords.
The negative correlation with intensity velocity is more difficult to explain. One possible
explanation is that intensity that is more flat across the span of the sentence indicates less
dramatic modulation of the vocal source by the articulators of the vocal tract, i.e., more
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coarticulation, more frequent consonant lenition, etc. More research is necessary to understand
the significance of this measure.
Talker gender The effect of talker gender on intelligibility is also statistically significant in this
model, suggesting that, once the aforementioned speech parameters related to intensity, pitch,
and the vowel space are accounted for, some aspect remains that makes the male talkers more
intelligible than the females in this corpus. One possible locus of this difference is spectral
properties of speech other than formant center frequencies, such as the bandwidths of the
formants or the density of the harmonics of
0
both properties that are obscured when
focusing on the F2×F1 vowel space defined only by the center frequencies of each formant.
Another possibility is that consonant articulation varies systematically across gender groups in a
way that impacts intelligibility in our sample of talkers. More research is needed to determine
what factor(s) underlie this result.
DISCUSSION
This paper presents the results of a within- and across-dialect speech perception study involving
talkers and listeners from the Pacific Northwest (PN) and Northern Cities (NC) dialects of
American English. These dialects are known to differ phonolexically in several respects, but in
general the differences are regarded as fairly subtle and the dialects are judged to be mutually
intelligible (indeed, many of the listeners in our study did not even notice the presence of out-of-
dialect speakers when debriefed after the perception task). Consistent with this view, our
findings showed no systematic difference in intelligibility attributable to talker-listener dialect
difference, but dramatic individual differences in talker intelligibility within talker dialect groups
in the +2 dB SNR condition. There was a small difference in intelligibility between the talker
populations (with PN speech being slightly more intelligible than NC speech) even after
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accounting for a variety of acoustic predictors. That result is consistent with findings from
Clopper and Bradlow (2008), who reported higher intelligibility for General American talkers
than for Northern talkers, but cannot be easily related to the findings of Jacewicz and Fox (2014),
who did not include Northern / Northern Cities speech in their study.
It is worth noting in this context that the similarity of the two dialects made the task
easier than it might have been if the dialects had been more divergent. This is evidenced by the
need for a fairly low SNR (+2 dB) to elicit a sufficient number of errors in the perception task.
This is a level that can be readily found in daily life (cf. Hodgson, Steininger, & Razavi, 2007);
with stronger divergence, dialect effects on intelligibility may have been more observable at this
level. It is also worth noting that the results presented here may not generalize to speech
perception tasks at more challenging SNRs, as the different acoustic dimensions discussed may
not degrade at the same rate as noise increases.
Summary of acoustic findings
In this study, more intelligible talkers tended to have larger convex hulls encompassing their
vowel spaces, smaller mean vowel cluster sizes, lower repulsive force of their vowel systems,
and smaller vowel polygons defined by F1 and F2 means. More intelligible talkers also tended to
have larger overall pitch ranges, larger average pitch range magnitudes across sentences, higher
mean pitch velocities, and lower mean pitch dynamicities. Higher intelligibility was also
correlated with higher values of mean intensity dynamicity, and by lower (i.e., more negative)
values of mean intensity velocity. Finally, there was a slight tendency for males to be more
intelligible than females in this sample of talkers.
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Variation in intrinsic intelligibility
The fact that we found variation in intrinsic intelligibility across talkers within the PN/NC corpus
is hardly surprising. It is well documented that talker-specific variation can influence the
intelligibility of a particu
intelligible than others (e.g., Bradlow et al., 1996; Payton et al., 1994; Picheny et al., 1985).
However, the relative benefit of talker-specific variation derives from a complex talker-listener
tion accuracy is higher
when the talker is held constant than when the talker varies across trials (Goldinger, Pisoni, &
Logan, 1991; Nygaard, Sommers, & Pisoni, 1995; Palmeri, Goldinger, & Pisoni, 1993). This is
usually interpreted as a benefit of exposure or training. This benefit appears to derive from
dynamic-pronunciation aspects of the signal production rather than from production-independent
variation, such as post-recording manipulations (Bradlow et al., 1996; Church & Schacter, 1994;
Sommers & Barcroft, 2007)
idiosyncratic pronunciation traits to aid in performing the speech perception task. Talker-
familiarity benefits are particularly robust when the listener is intimately familiar with a talker,
as occurs in long-term friendships or in marriages; moreover, intimate-familiarity becomes more
important under adverse listening conditions (Souza, Gehani, Wright, & McCloy, 2013).
The strong impact of listener experience on talker intelligibility raises the question of
whether listener experience affects intelligibility in cross-dialect listening situations, and whether
experience, exposure or training with a dialect might generalize to novel speakers of that dialect,
as has been shown for foreign accented speech (Bradlow & Bent, 2008). More relevant to this
study, however, is the question of how to quantify variability at the dialect level (and any listener
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adaptation to dialect) in the face of talker-level variability and the known ability of listeners to
adapt to individual talkers.
Separating dialect from idiolect
In creating stimulus corpora for speech perception research, there is an inherent trade-off in the
amount and type of variability to include. On one end of the spectrum, large numbers of words or
sentences are recorded from a single talker (at the expense of talker variability); at the other end
of the spectrum, many different talkers are recorded saying just a few words or sentences each
(at the expense of stimulus variability). If what is of interest is the difference between groups
(e.g., age groups, genders, dialects, familiar/unfamiliar talkers), different talkers must necessarily
be recruited to represent each of those groups. This introduces a potential source of error: the
groups become confounded with the identity of the talkers representing those groups, and any
difference (or lack thereof) may be attributable to the idiosyncrasies
than the aspects of their speech that define them as a member of the group they represent. Put
another way, it becomes difficult to separate idiolect, on one hand, from dialect or sociolect on
the other.
The ideal solution to this problem is a sufficiently large number of talkers in each group
such that the idiolectal variation averages out within groups, allowing cross-group differences to
emerge. However, practical limitations prevent sample size from expanding indefinitely, and in
any case it is not always obvious how much idiolectal variation is expected on the dimensions of
interest, nor is it always clear how large a sample of talkers is necessary to adequately represent
this variation. In this study we used ten talkers per dialect group; Clopper and Bradlow (2008)
used six, Jacewicz and Fox (2014) used four. Whatever other advantages our methods may have
over those previous studies, it is clear that even ten talkers per dialect was not enough to ensure
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equivalence of the samples on aspects of intelligibility that are unrelated to dialect. Our statistical
methods and acoustic measurements allowed us to deal with this aspect of our sampling, make
some observations about which aspects of talker variability were most predictive of intelligibility
differences, and detect a small effect of talker dialect on intelligibility once those acoustic
differences had been accounted for. However, we are still unable to say to what extent that
finding reflects genuine intelligibility differences between the dialects, or merely residual aspects
of the talker idiolects in our sample that were not accounted for by the other acoustic predictors.
Any inference to the intelligibility of dialects generally is therefore infeasible without a larger-
scale symmetrical study encompassing many more dialects and many more talkers than were
included here.
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