Harmonizing different metrics for speech privacy
Markus MÜLLER-TRAPET(1)(1) National Research Council, Canada, markus.mueller-trapet@nrc.ca

Abstract
It is regularly debated which metric to use for the assessment of speech privacy in open-office as well as closedoffice environments. Metrics commonly used for this purpose include the Privacy Index (PI), which is derived
from the Articulation Index (AI) determined according to ASTM E1130 as well as the Speech Privacy Class(SPC) determined according to ASTM E2638. Regardless of the method of determining these values, it has
recently been shown that both metrics are mathematically related. In light of this information, it seems that thediscussion might be better directed at different questions, for example what the recommended values should befor different environments (open-plan, closed or mixed). This paper will highlight the similarity between speechprivacy metrics as well as the commonly recommended values for open-plan and closed offices and how they
relate to each other. The analysis and discussion is based on data from extensive field measurements as well as
measurements under laboratory conditions. The data of the field measurements is also compared to subjective
responses to assess the perceived privacy.
Keywords: Speech privacy, Open-plan offices, Closed offices

1

INTRODUCTION

A major reason for complaints of workers in open-plan offices (and to some degree in closed offices) is thedisturbance by speech from their co-workers causing distraction [1]. It is hence crucial for acoustic practitioners
to be able to accurately predict and assess the perceived speech privacy in offices. Several metrics have beendeveloped to asses speech privacy, all of which reflect the fact that speech intelligibility, which is an importantfactor influencing the amount of distraction caused by speech, is mostly determined by the ratio between the
speech energy reaching the listener and the energy of the background noise at the listener position [2, 3].
This paper uses an extensive set of field and laboratory measurement data (Section 2) to examine three speechprivacy metrics (Section 3) and to show that because of the similarity of the approaches, there is a clear
relationship between all metrics (Section 4). This fact is used in analyzing the recommended values for thedifferent metrics and in pointing out discrepancies between them (Section 5). In order to relate the findings tothe actual perceived speech privacy, subjective data in the form of responses to questionnaires by the workers
in open-plan offices are compared to the objective data (Section 6).

2

OBJECTIVE AND SUBJECTIVE DATA USED IN THIS STUDY

The data used in this paper to highlight the relationship between speech privacy metrics is from a large-scale
study conducted by the National Research Council (NRC) of Canada from 2008 to 2013 in 24 buildings inNorth America [4, 5]. The study, called the “Post-occupancy evaluation of green and conventional buildings”
and subsequently referred to in short as the GreenPOE study, involved objective measurements of indoor environment parameters, including acoustic measurements related to speech privacy, as well as an online questionnaire of the people working in the buildings where measurements were made. The questionnaire covered generalquestions about job satisfaction and stress as well as more specific questions related to the satisfaction with theindoor environment in the offices. For this paper, only the questionnaire items relating to speech privacy havebeen evaluated.
The objective data from the GreenPOE study yields almost 1300 datasets for open-plan offices with rather low
speech privacy. To also include data representative of offices with higher speech privacy, such as closed offices,2398

an additional 500 datasets from laboratory measurements at the NRC are used in this paper. The laboratory
data was gathered from measurements made on different wall types together with artificially created backgroundnoise of varying sound pressure level (SPL).

3

SPEECH PRIVACY METRICS

In this section, the most common metrics used for assessing speech intelligibility and speech privacy in buildings
are summarized. The metrics include the Privacy Index (PI), the Speech Intelligibility Index (SII) as well as the
Speech Privacy Class (SPC).
3.1 Privacy Index (PI)The Privacy Index (PI) is a measure of the speech privacy as it relates to a lack of intelligible speech, i. e. distraction, reaching the listener’s ears. As such, the PI is defined as the complementary value of the Articulation
Index (AI) [6, 7] as
PI = 100 · (1 − AI) ,(1)
the AI itself being a measure of speech intelligibility with values between 0 and 1, so that the PI ranges
between 0 and 100.
The factors involved in determining AI are the speech source reference SPL LS , the level difference LD measured for a certain position of the sound source and the receiver, as well as the background noise SPL LN .
These values are used to determine the effective Signal-to-Noise Ratio (SNR) at the listener position. Following
ASTM E1130, the speech source reference SPL for “normal” vocal effort has to be used, leaving LD and LN
as free variables. The variables of LS , LD and LN are all considered for the third-octave bands with center
frequencies between 200 Hz and 5 kHz. As a single number rating, the AI (and thus PI) is then calculated as aweighted sum across frequency, with the frequency-weights specified in ASTM E1130.
Recommended values of the PI for normal speech privacy in open-plan offices are listed in Appendix X2 of
ASTM E1130 as 80 to 95. Values of PI above 95 are mentioned in the same Appendix as representing confidential speech privacy.
3.2 Speech Intelligibility Index (SII)The Speech Intelligibility Index (SII) is a measure of speech intelligibility like the AI, and it has replaced the
AI method in ANSI S3.5 [8]. While the basic input data and the frequency-weights for the SII are essentiallythe same as for the AI, the method for the SII additionally includes the psychoacoustic effect of frequency
masking to determine the effective SNR at the listener’s ears.
The SII has originally not been defined in terms of speech privacy, but in analogy to the PI the complementary
value to the SII can be used. Recommended values for the SII in open-plan offices have been suggested fornormal and confidential speech privacy as 0.2 and 0.1, respectively [9].
3.3 Speech Privacy Class (SPC)
As was mentioned above, the reasoning for using the PI as a measure of speech privacy is that it represents alack of speech intelligibility, given that it is the complementary value to the AI. However, in situations with alow degree of speech intelligibility perceived speech privacy may not be as high as desired, as audible speech
may still reach the listener [10].
To address this problem, the Speech Privacy Class (SPC) was developed for use especially in situations withlow speech intelligibility, such as closed offices and meeting rooms. The SPC and the associated measurement
method are defined in the standard ASTM E2638 [11]. The input data to determine the SPC is the measured
level difference between the average SPL inside the closed room and the SPL at individual receiver positions
outside of the room, as well as the background noise SPL at the receiver positions. The criterion values for the
SPC are then chosen depending on the desired level of speech privacy [12].

2399In Appendix X2 of ASTM E2638, recommended values for the SPC are listed for different levels of speechprivacy in closed rooms. For minimal and standard speech privacy the recommended values are 70 and 75,
respectively.

4

RELATIONSHIP BETWEEN SPEECH PRIVACY METRICS

Since all of the commonly used speech privacy metrics are based on the effective SNR at the listener position,
it is to be expected that there is a relationship between the metrics. Due to the fact that either a different
frequency range is used or the frequency-weights differ between metrics, any relationship found will be an
approximation. Nevertheless, it is shown below that relatively simple functions relating PI to SPC and SII canbe found and that the agreement with experimental data is very good over the relevant range of values.
4.1 PI and SPC
The relationship between the PI and SPC has recently been demonstrated and validated with the data mentioned
in Section 2 [13]. The following function to calculate the PI from the SPC was determined to give good
accuracy over the entire range of SPC values (R2 = 0.992):



6.65
· SPC − 2.8 ,(2)PI ≈ 100 · 0.5 + 0.5 · erf
100
where erf is the error function. Fig. 1 shows the relationship between PI and SPC according to Eq. 2 together
with the experimental data in open-plan and closed offices mentioned in Section 2. It can be seen that theagreement of Eq. 2 with experimental data is very good. The effect of limiting the SNR for the calculation of
PI also becomes apparent for values of PI below 20 and above 80.

100
90

R2 = 0.992

80
70

PI

60
50
40
30
20

Open-plan offices (Field)
Closed offices (Lab)Sigmoid function (SP C0 = 42.2, k = 3.75)

10
0
10

20

30

40

50

60

70

80

SPC
Figure 1. Relationship between SPC and PI for experimental data and according to Eq. 2
The inverse relationship of Eq. 2 was found in [13] as follows:


PI
100
· erf−1− 1 + 42.2 ,
SPC ≈
6.65
50

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(3)

where erf−1 is the inverse error function. It has to be mentioned that the inverse relationship in Eq. 3 is notreliable for PI values below 5 or above 95, as the inverse error function tends towards negative and positive
infinity, respectively, beyond these values. This represents the effect of limiting the SNR range in the calculationof the PI, which effectively leads to a loss of information.
4.2 PI and SII
From the raw data mentioned in Section 2, the SII was calculated in addition to the PI and SPC. This allows tocompare the values of PI and SII and establish the relationship between these two metrics. It was found that asimple second order polynomial function describes SII as a function of AI very well (R2 = 0.998), which gives
the following function:
SII ≈ 0.28 · AI 2 + 0.69 · AI .
(4)
By replacing AI in Eq. 4 according to Eq. 1 as AI = 1 − PI/100, an expression of SII as a function of PI is
obtained:


PI
PI 2
− 1.25 ·+ 0.97 .
(5)
SII ≈ 0.28 ·100
100
The relationship between PI and the complementary values of SII (i. e. 1 − SII) is shown in Fig. 2 for theexperimental data and according to Eq. 5. The good agreement of the calculated values with experimental data
can be verified in the graph. While the agreement is slightly worse as PI tends toward zero, the agreement
for higher values of PI, which are most relevant for the assessment of speech privacy, is very good. Overall,for 99 % of the data there is a deviation of less than 0.03 between the measured values of SII and the values
calculated from PI according to Eq. 5.

1.0
0.9
0.8

R2 = 0.998

0.7
1-SII

0.6
0.5
0.4
0.3
0.2

Open-plan offices (Field)
Closed offices (Lab)Model function SII = 0.28 ·

0.1
0.0

0

10

20

30

40

50
PI

60


PI 2
PI
− 1.25 · 100+ 0.97
100

70

80

90

100

Figure 2. Relationship between PI and SII for experimental data and according to Eq. 5
The relationship between PI and SII can be inverted (as was done for Eq. 2 and Eq. 3), to give the following
expression to calculate PI from SII:r
PI ≈ 100 · 2.23 −!
SII+ 1.52 .
0.28

(6)

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5

DISCREPANCIES BETWEEN RECOMMENDED VALUES OF SPEECH PRIVACYMETRICS

Given the relationship between the speech privacy metrics detailed above, it should be ensured that the recommended values for all metrics are in alignment, so that no conflicting results are obtained depending on which
metric was chosen to evaluate speech privacy.
In the following, three tables are provided which list the recommended values for each speech privacy metricand the converted values for the other two metrics according to the relationships presented above. Table 1 lists
the recommended values for PI, Table 2 lists the recommended values for SII and Table 3 lists the recommended
values for SPC. In each of the tables, the original recommended values are underlined.
Table 1. Recommended values for PI in open-plan offices (underlined) according to ASTM E1130, and corresponding values for SPC and SII, converted according to Eq. 3 and Eq. 5, respectively.
Level of Speech Privacy
NormalConfidential

PI
80–95
95–100

SPC
51–60
> 60

SII
0.15
0.035

Table 2. Recommended values for SII in open-plan offices (underlined), and corresponding values for PI and
SPC, converted according to Eq. 6 and Eq. 2, respectively.
Level of Speech Privacy
NormalConfidential

SII
0.2
0.1

PI
74
86

SPC
49
54

Table 3. Recommended values for SPC in closed offices (underlined) according to ASTM E2638, and corresponding values for PI and SII, converted according to Eq. 2 and Eq. 5, respectively.
Level of Speech Privacy
MinimalStandard

SPC
70
75

PI
100
100

SII
0
0

An important conclusion that can be drawn from the data in Tables 1 and 2 for open-plan offices is that therecommended values of SII do not agree well with the recommended values for PI. Converting the values for
PI from ASTM E1130 to SII using Eq. 5 yields substantially lower values than the recommended values for
SII. This shows that depending on the metric used to assess speech privacy in an open-plan office, different
results may be achieved.
For closed office spaces, the data in Table 3 shows that no meaningful distinction between minimal and standard
speech privacy (or higher levels of speech privacy) can be assessed by PI and SII, as the values for PI are all
equal to 100 and the values for SII are all equal to zero, whereas SPC values are increasing according to
the level of privacy. This indicates that the speech intelligibility may be zero but speech sounds may still be
audible.
It can thus be concluded that there is a need to harmonize the recommended values of speech privacy metrics,
and that the SPC is the only metric which can be used in scenarios of high speech privacy, whereas all metrics(PI, SII and SPC) can be used for lower speech privacy.

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6

SUBJECTIVE SPEECH PRIVACY

Given that the existing metrics and the associated recommendations may not lead to consistent results, it isimportant to also consider the subjective speech privacy perceived by the people working in offices. As was
mentioned in Section 2, the GreenPOE study, which yielded the objective measurement data used above, also
included a questionnaire to obtain subjective responses from the people working in the offices where the data
was measured. In the questionnaire, workers were asked to rate the agreement with general statements about
their work satisfaction, and to respond to more detailed questions related to different aspects of the indoor
environment, such as lighting, air quality, and acoustics.
For the data presented in this paper, the subjective ratings to the questions related to the office acoustics were
evaluated and compared to the results for the objective metrics. In all questions, a 7-point rating scale wasused, with categories either corresponding to a degree of satisfaction or to the agreement with a statement orquestion, from “Not at all” (1) to “Very” (7). Since it was not always possible to link the survey data to theobjective measurements at individual workstations, average results for each building will be used both for theobjective as well as the subjective data.
In general, significant correlation (p < 0.01) between the subjective ratings and the objective results for the threespeech privacy metrics (PI, SII and SPC) was found. The degree of correlation was very similar between all
metrics, so here only the example for the PI will be shown. In Fig. 3 the average subjective ratings for thequestion “At your workstation, how understandable are overheard conversations and phone calls from others inyour building?” are shown for all 24 buildings as a function of the average PI in each building. The 95 %confidence interval for each average subjective rating is indicated by the error bars. The correlation between PI
and the responses for this question (R2 = 0.65) was highly significant (p < 0.0001). The linear regression line
is also plotted in Fig. 3.

Rating: Not at All – Very

7
6
5
4
3
2
1
25

Data
Linear Regression (R2 = 0.65, p < 0.0001)

30

35

40

45

50

55

60

65

70

PI
Figure 3. Relationship between average PI and average subjective response per building to the question “At yourworkstation, how understandable are overheard conversations and phone calls from others in your building?”.
The 95 % confidence interval for each average subjective rating is indicated by the error bars.

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Given that the recommended values for the speech privacy metrics mentioned in Section 5 were reached at
almost none of the measured workspaces (see data in Figs. 1 and 2), it was to be expected that the perceived
speech privacy would be unsatisfactory. This is supported by the subjective responses in Fig. 3, where none ofthe data points correspond to a positive perception of the speech privacy, which in this case would be represented by low scores (conversations not understandable). However, the significant correlation between objective
and subjective data shows that the perceived speech privacy can be assessed with the objective metrics, where
higher values of the objective metrics correspond to a higher subjective rating of speech privacy.

7

CONCLUSIONSThe three common metrics to assess speech privacy in offices — PI, SII and SPC — have been examinedin this paper and a relationship between all three metrics has been established based on an extensive set ofmeasurement data from the field and the laboratory. In addition, the relationship between objective results andthe perceived level of speech privacy by office workers has been demonstrated using responses from surveys
performed in the same buildings where the objective measurements were carried out.
It was shown that a clear relationship between PI, SII and SPC exists and that values of one metric can thus beconverted into values of the other metrics with a high degree of confidence. Using the established relationship,
it was pointed out that the values typically recommended to achieve a certain level of speech privacy are notconsistent between the three metrics. This is problematic since it is then possible that the actual perceived
speech privacy in an office will depend on which metric was used to predict and assess the speech privacy
during the design phase. The conclusion is that the decision which metric to use for the assessment of speechprivacy in open-plan offices is typically not relevant. It is far more important to ensure that the recommendations
for target values of the metrics are consistent and yield the desired level of perceived speech privacy.
It was also shown that only the SPC is useful in assessing high levels of speech privacy, because the othermetrics impose an artificial limit on the receiver SNR which leads to a loss of information. This means that the
PI and SII are not valid metrics for closed offices and meeting rooms, where speech privacy is typically higher.
The evaluation of the subjective responses regarding speech privacy has shown that there is a significant correlation between the objective and the subjective data, which confirms that the speech privacy metrics are usefulfor assessing the perceived performance. In agreement with the objective data, the subjective responses haveshown that office workers in open-plan offices are not satisfied with the level of speech privacy. However, giventhat worker satisfaction does improve with an increase of the objective values, it should be possible to design
open-office workplaces that perform better in terms of speech privacy. One of the first steps toward that goal
will be to harmonize and verify the recommended values for the speech privacy metrics so that designers havea reliable framework to work with.

ACKNOWLEDGMENTS
The author gratefully acknowledges the support by Chantal Arsenault who provided the raw field measurement
data, and by Theresa Reif who evaluated the subjective response data. The original research (GreenPOE study)
from which the field data was taken was generously supported by: the National Research Council Canada;
the Natural Resources Canada Program of Energy Research and Development (PERD); the Public Works andGovernment Services Canada; the governments of Alberta, Manitoba, Nova Scotia, New Brunswick, Ontario andSaskatchewan; BC Hydro; the Fonds en efficacité énergétique de Gaz Métro; Haworth; the Jim H. McClung
Lighting Research Foundation, Inc.; and the University of Idaho – Integrated Design Lab.

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