VOICE BIOMETRICS WITHIN THE FAMILY: TRUST, PRIVACY
AND PERSONALISATION
Delphine Charlet
France Telecom
2 avenue Pierre Marzin, 22307 Lannion Cedex, France
Victor Peral Lecha
France Telecom
Building 10, Chiswick Business Park, 566 Chiswick High Road, London W4 5XS, United-Kingdom
Keywords:
Voice biometrics, speaker recognition, privacy, trust, personalisation, family voices evaluation.
Abstract:
Driven by an increasing need for personalising and protecting access to voice services, the France Telecom
R&D speaker recognition system has been used as a framework for experimenting with voices in a family
context. With the aim of evaluating this task, 33 families were recruited. Particular attention was given to 2
main scenarios: a name-based and a common sentence-based scenario. In each case, members of the family
pronounce their complete name or a common sentence respectively. Moreover, this paper presents a database
collection and first experiments for family voices.
The results of this preliminary study show the particular difficulties of speaker recognition within the family,
depending on the scenario, the genre and age of the speaker, and the physiological nature of the impersonation.
1 INTRODUCTION
Privacy within a family is a sensible subject: each
member of a family may require some privacy,
but achieving privacy protection through traditional
means (such as pin code) may be resented by the
other members of the family. Indeed, traditional pri-
vacy protection is based on “what you know” infor-
mation, thus, to be effective, it needs to be hidden
from the others. So the use of traditional authenti-
cation password or PIN is inadequate with the global
feeling of mutual trust that often exists within the
family. Hence, speaker recognition appears to be an
interesting way for privacy protection since it is based
on “what-you-are” and not on “what-you-hide”. On
the other hand, the genetic links that exist between
parents and children as well as the same cultural and
geographical contexts they share may hamper speaker
recognition, as it is pointed by (van Leeuwen, 2003).
Moreover, children constitute a difficult target for
speech recognition in general (Wilpon and Jacobsen,
1996).
As far as we know, no published study has been
done on this subject. Thus, for the purpose of this
preliminary study we collected a database of family
voices in order to perform technological evaluation of
the performances of speaker recognition in the con-
text of family voices.
2 APPLICATION DESIGN
We have defined a standard application to evalu-
ate the feasability to personalise home telecoms ser-
vices for each member of a family, based on voice
recognition. It has been decided that personalisation
should be done through an explicit step of identifica-
tion/authentication. Thus, if this step is explicit, it has
to be as short as possible. These ergonomic consider-
ations lead us to two technological choices:
• Identification and authentication performed on the
same acoustical utterance.
• Text-dependent speaker recognition: as it achieves
acceptable performances for short utterances (less
than 2 seconds).
Two scenarios were defined, depending on the con-
tent of the utterance pronounced by the speaker.
2.1 Name-based scenario
In this scenario, the user pronounces his/her first and
last name to be identified and authenticated. The main
characteristics of such an application are:
• It is easy to recall.
• It is usually rather short.
• It enables deliberate impostor attempts: e.g. a
mother can claim to be her daughter.
330
Charlet D. and Peral Lecha V. (2005).
VOICE BIOMETRICS WITHIN THE FAMILY: TRUST, PRIVACY AND PERSONALISATION.
In Proceedings of the Second International Conference on e-Business and Telecommunication Networks, pages 330-334
DOI: 10.5220/0001407003300334
Copyright
c
 SciTePress

As for each member of the family, the phonetic
content of the voiceprint is different, the identifica-
tion process is performed on both the voice and the
phonetic context, although there is no explicit name
recognition process. Thus it can be expected that the
identification error rate will be negligeable. On the
other hand, as the authentication is performed on a
short utterance (e.g. 4 syllables), the authentication
performances are expected to be rather poor.
2.2 Common sentence-based
scenario
In this scenario, the user pronounces a sentence,
which is common to all the members of the family.
The main characteristics of such an application, com-
pared to the name-based scenario are:
• It is less easy to recall: the sentence should be
prompted to the user, if he does not remember.
• The length of the sentence may be longer than a
simple name.
• It prevents deliberate impostor attempts: e.g. a
mother can not claim to be her daughter.
As for each member of the family the phonetic con-
tent of the voiceprint is the same, the identification
process is performed only on the voice. Thus it can
be expected that the identification error rate will be
higher than in the context of name-based recognition.
On the other hand, as the authentication is performed
on a longer sentence (10 syllables), the authentication
performances are expected to be better than those of
the name-based scenario.
3 DATABASE DESIGN
3.1 Family profile
The families were required to be composed of 2 par-
ents and 2 children. The children are older than 10.
They all live in the area of Lannion, where this work
was conducted. 33 families were recruited: 19 with
one son and one daughter, 10 with 2 sons and 4 with 2
daughters. They were asked to perform 10 calls from
home, with their landline phone, during a period of
one month. Hence, factors such as voice evolution
over time or sensitivity to call conditions are not stud-
ied in this work.
3.2 Name-based scenario
In this scenario, the key point is the fact that there
might be deliberate impostor attempts on a target
speaker, as the user claims an identity to be recog-
nised.
3.2.1 Training
For each member of a family, training is performed
with 3 repetitions of his/her complete name (first +
last name). This number of repetitions represents a
good trade-off between performances and tediousness
of the task.
3.2.2 Within family attempts
Each member of a family is asked to perform attempts
on his own name, and also attempts on the name of
each of the other members of his family.
3.2.3 External impostor attempts
A set of impostor attempts from people who do not
belong to the family is collected. The external im-
postors are composed of members of other families
who pronounced the name of the member of the tar-
get family.
3.2.4 Collected Database
As we focus on speaker recognition, we only retained
the utterances where the complete name was correctly
pronounced.
• 16 families completed their training phase.
• 13 families completed the testing phase also.
• 672 true speaker attempts collected for the 13 fam-
ilies, that makes an average of 52 true speaker
attempts per family, thus an average of 13 true
speaker attempts per user.
• 582 within family impostor attempts collected for
the 13 families, that makes an average of 45 within
family impostor attempts per family, thus an aver-
age of 11 within family impostor attempts per user.
• 2173 external impostor attempts (impostor at-
tempts are performed on all the families who have
completed the training phase)
3.3 Common sentence-based
scenario
In this scenario, the key point is the fact that there
can not be deliberate impostor attempts on a target
speaker, as the user does not claim identity to be
recognised.
3.3.1 Training
For each member of a family, training is performed
with 3 repetitions of the common sentence.
VOICE BIOMETRICS WITHIN THE FAMILY: TRUST, PRIVACY AND PERSONALISATION
331

3.3.2 Within family attempts
Each member performs attempts by pronouncing the
common sentence.
3.3.3 External impostor attempts
The attempts of other families are used to perform ex-
ternal impostor attempts.
3.3.4 Collected Database
As we focus on speaker recognition, we only retain
the utterances where the sentence was correctly pro-
nounced.
• 25 families completed their training phase.
• 17 families completed the testing phase also.
• 614 within family attempts collected for the 17
families, that makes an average of 36 attempts per
family, thus an average of 9 attempts per user.
• 13549 external impostor attempts: all within family
attempts are used to perform impostor attempts on
the other families (impostor attempts are performed
on all the families who have completed the training
phase).
4 EXPERIMENTS
4.1 Speaker recognition system
For these experiments, the text-dependent speaker
recognition system developed in France Telecom
R&D is used. It basically relies on HMM modelling
on cepstral features, with a special care on variances,
with a speaker-independant contextual phone loop as
reject model (Charlet et al., 1997). The task is open-
set speaker identification. There was no particular
tuning of the system to this new task. The rejection
thresholds are set a posteriori, common to all fami-
lies.
4.2 Typology of errors
4.2.1 Name-based scenario
In this scenario, the identity claim is taken into ac-
count in the typology of errors. Within the family,
when the speaker A claims to be speaker A (pro-
nouncing the name of speaker A), the system can:
• accept the speaker as being speaker A: correct ac-
ceptance (CA)
• accept the speaker as being speaker B: false identi-
fication error (FI)
• reject the speaker: false rejection error (FR)
Moreover, in this scenario, within the family, a
speaker can make deliberate impostor attempts on a
target speaker. When the speaker A claims to be
speaker B, the system can:
• reject the speaker: correct rejection on internal im-
personation (CRII)
• accept the speaker as being speaker B: wanted false
acceptance on claimed speaker (WFA)
• accept the speaker as being speaker A: unwanted
correct acceptance (UCA)
• accept the speaker as being speaker C: unwanted
false acceptance (UFA)
• Internal False Acceptance is defined as: IFA =
WFA + UFA
Outside the family, when an impostor claims to be
speaker A, the system can:
• reject the speaker: correct external rejection (CER)
• accept the speaker as being speaker A: external
false acceptance on claimed speaker (EWFA)
• accept the speaker as being speaker B: external un-
wanted false acceptance (EUFA)
• External False Acceptance is defined as: EFA =
EWFA + UFA
4.2.2 Common sentence-based scenario
This is the classical typology of open-set speaker
identification. Within the family, when the speaker
A pronounces the common sentence, the system can:
• accept the speaker as being speaker A: correct ac-
ceptance (CA)
• accept the speaker as being speaker B: false identi-
fication error (FI)
• reject the speaker : false rejection error (FR)
Outside the family, when an impostor pronounces
the common sentence, the system can:
• accept the speaker as being speaker A: external
false acceptance error (EFA)
• reject the speaker: correct external rejection (CER)
5 RESULTS
5.1 name-based scenario
Figure 1 plots EFA, IFA and FI as a function of FR
for the name-based system.
For the particular operating point where {
FR=7.9%; FI=0.4%; IFA=21.1%; EFA=8.1%}, let us
see the details of errors:
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332

7 8 9 10 20 30
false rejection rate
0.4
0.7
1.0
2.0
4.0
7.0
10.0
20.0
error rate
FI
IFA
EFA
Figure 1: Name-based speaker identification within the
family
• FI=0.4% is low and due to 2 types of surprising
errors: a man identified as his wife and a 10-year-
old girl identified as his father.
• UCA=20.6% : when a member within the family
claims to be another member, he is “unmasked”
(i.e. truly identified) in 20% of the cases. This is
mainly due to the fact that, as the voiceprint of the
members of the family shares important part of the
phonetic content (same last name), an attempt with
another first name may match reasonably well the
voiceprint of the speaker if the first names are not
too different.
• IFA=21.1% - EFA=8.1% : the rate of IFA (false ac-
ceptance from deliberate impostor attempts within
the family) is more than 2.5 times as much as EFA
(from deliberate impostor outside the family, with
the same age/gender distribution as the impostors
within the family). Although we cannot quantify
what is due to line effects from what is due to phys-
iological resemblance, we can note that the system
is much more fragile to impostor within the same
family than from outside the family.
IFA is decomposed into WFA=18.0% and
UFA=3.1%. Table 1 presents the false acceptance
rate for deliberate impersonation (WFA : when
speaker A claims the name of speaker B and is
accepted as being speaker B) within the family, for
the different types of speaker. “Son” can do impostor
attempts on “son” when they are brothers. In the
same way, “daughter” can do impostor attempts on
“daughter” when they are sisters.
We analyze each type of speaker (father, mother,
daughter, son) with respect to their ability to defeat
the system and to their “fragility” to impostor at-
tempts (Doddington et al., 1998). In the table, be-
tween brackets are given the numbers of tests of each
type of imposture, in order to give an idea of the reli-
ability of the results.
Table 1: false alarm rate according to the type of impostor
and target speaker
Impostor target speaker
speaker father mother son daughter
father – 11.8 12.3 2.2
[51] [57] [45]
mother 0 – 14.8 18.6
[52] [54] [43]
son 7.4 29.8 29.4 53.3
[54] [47] [17] [30]
daughter 0 32.6 50 50
[45] [43] [36] [8]
From the table, the following remarks can be done:
• the father, as an impostor, gets a moderate and
equal success rate on his wife and his son, and a low
success on his daughter. As a target, he is “fragile”
against his son at a moderate level.
• the mother, as an impostor, gets a better success
rate than her husband on her children, and a com-
plete failure on her husband. The difference of suc-
cess rate between the son target and the daughter
target is not high. As a target, she is fragile against
her husband at a moderate level, and she is equally
fragile at a high level against her children.
• the son, as an impostor, has a low success rate on
his father, a high level on his mother and brother
and a very high level on his sister (the difference
between brother and sister success rate might not
be significant because of the small number of at-
tempts in the case of 2 brothers attempts). As a
target, he is moderately fragile against his parents
and highly against his sister.
• the daughter, as an impostor, has a high success rate
on her mother and a very high success on her sister
or brother, and a complete failure on her father. As
a target, she is not fragile against her father, mod-
erately against her mother and very highly against
her sister or brother.
5.2 common sentence-based scenario
Figure 2 shows the global performances of the com-
mon sentence-based system, for different operating
points.
For a particular operating point where globally
{FR=6.1%; FI=1.2%; EFA=5.8%;}, analyzing the re-
VOICE BIOMETRICS WITHIN THE FAMILY: TRUST, PRIVACY AND PERSONALISATION
333

2 4 6 8 10 20
false rejection rate
0.4
0.7
1.0
2.0
4.0
7.0
10.0
20.0
error rate
FI
EFA
Figure 2: Common sentence-based speaker identification
within the family
sults family per family, we observe that over the 17
families who made identification attempts:
• 13 families have no false identification : FI=0%
• 3 families get a false identification rate : FI=2-2.5%
(actually 1 observed error)
– family #13: father identified as his 17-year-old
son
– family #16: 14-year-old girl identified as her
mother
– family #20: mother identified as her 20-year-old
girl
• 1 family get a false identification rate of FI=7% due
to one unique cause of error: a 12-year-old girl was
identified as her mother
Speaker identification within a family appears to be
easily achievable for a majority of the families tested
(13/17). For the remaining families, where errors
were observed, the errors are not frequent and always
concern a specific pair parent/same-sex teenager.
6 DISCUSSION
In the name-based scenario, there is deliberate imper-
sonation within the family. As the speech utterance
to perform speaker authentication on is very short
(on average 4 syllables) performances are very poor.
However, the analysis of the differences of perfor-
mances according to the type of impostor and target
speaker is interesting. It shows that the most “fragile”
to impersonation are the children, between them, and
that they are also the most effective impostors.
In the common sentence-based scenario, there can-
not be deliberate impersonation, as there is no identiy
claim. Except for some rare cases where there is an
identification error, it seems to be the most effective
and ergonomic way to perform speaker recognition
within the family. Considering the case of identifica-
tion errors, as they always occured on the same pair,
it should be possible to develop special training pro-
cedure to prevent them.
7 CONCLUSION
In this paper, we have presented a database of fam-
ily voices collected for voice recognition within the
family. Two scenarios are studied. The number of
recorded attempts is enough to perform experiments
and draw first conclusions about the particular diffi-
culties of speaker recognition within the family.
Despite the novel nature of the application, voice
biometrics seems the most natural way to manage the
privacy and trust issues when accessing to voice ser-
vices in a family context. Further research will at-
tempt to improve the reliability of the system when
dealing with the voices of children.
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