Performance Evaluation in Open-Set Speaker Identification.
ABSTRACT The concern in this study is the approach to evaluating the performance of the open-set speaker identification process. In
essence, such a process involves first identifying the speaker model in the database that best matches the given test utterance,
and then determining if the test utterance has actually been produced by the speaker associated with the best-matched model.
Whilst, conventionally, the performance of each of these two sub-processes is evaluated independently, it is argued that the
use of a measure of performance for the complete process can provide a more useful basis for comparing the effectiveness of
different systems. Based on this argument, an approach to assessing the performance of open-set speaker identification is
considered in this paper, which is in principle similar to the method used for computing the diarisation error rate. The paper
details the above approach for assessing the performance of open-set speaker identification and presents an analysis of its
- SourceAvailable from: citeseerx.ist.psu.edu[Show abstract] [Hide abstract]
ABSTRACT: Reynolds, Douglas A., Quatieri, Thomas F., and Dunn, Robert B., Speaker Verification Using Adapted Gaussian Mixture Models, Digital Signal Processing10(2000), 19–41.In this paper we describe the major elements of MIT Lincoln Laboratory's Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around the likelihood ratio test for verification, using simple but effective GMMs for likelihood functions, a universal background model (UBM) for alternative speaker representation, and a form of Bayesian adaptation to derive speaker models from the UBM. The development and use of a handset detector and score normalization to greatly improve verification performance is also described and discussed. Finally, representative performance benchmarks and system behavior experiments on NIST SRE corpora are presented.Digital Signal Processing 01/2000; 10(1-3-10):19-41. · 1.50 Impact Factor
Conference Paper: Open-set speaker identification under mismatch conditions.INTERSPEECH 2009, 10th Annual Conference of the International Speech Communication Association, Brighton, United Kingdom, September 6-10, 2009; 01/2009
Conference Paper: Open-set speaker identification using adapted Gaussian mixture models.INTERSPEECH 2005 - Eurospeech, 9th European Conference on Speech Communication and Technology, Lisbon, Portugal, September 4-8, 2005; 01/2005
Performance Evaluation in Open-Set Speaker
A. Malegaonkar1*, A. Ariyaeeinia2
1Auraya Systems Pty. Ltd. Sydney, Australia
2University of Hertfordshire, College Lane, Hatfield, Hertfordshire, UK
Abstract. The concern in this study is the approach to evaluating the
performance of the open-set speaker identification process. In essence,
such a process involves first identifying the speaker model in the
database that best matches the given test utterance, and then
determining if the test utterance has actually been produced by the
speaker associated with the
conventionally, the performance of each of these two sub-processes is
evaluated independently, it is argued that the use of a measure of
performance for the complete process can provide a more useful basis
for comparing the effectiveness of different systems. Based on this
argument, an approach to assessing the performance of open-set
speaker identification is considered in this paper, which is in principle
similar to the method used for computing the diarisation error rate. The
paper details the above approach for assessing the performance of open-
set speaker identification and presents an analysis of its characteristics.
best-matched model. Whilst,
In general, speaker identification is defined as the process of determining the correct
speaker of a given test utterance from a population of registered speakers [1-2]. If this
process includes the option of declaring that the test utterance does not belong to any
of the registered speakers, then it is specifically referred to as open-set speaker identi-
fication. An inherent feature of this process is that it provides the possibility of
establishing individuals’ identities without the need for any identity claims. This in
turn offers the capability for enhancing the security aspect of speaker verification
through the screening process. Such screening may be required at the enrolment phase
to minimise the possibility of multiple identity acquisition, or deployed at the
verification stage to increase the capability to detect access attempts by impostors.
* During the course of this work, Malegaonkar was with the University of Hertfordshire.
2 A. Malegaonkar, A. Ariyaeeinia
Given a set of registered speakers and a sample test utterance, this task is defined as a
twofold problem . Firstly, it is required to identify the speaker model in the
registered set that best matches the given test utterance. This is the process of
identification. Next, it is required to determine if the test utterance is actually
produced by the best matched speaker or it is originated by a speaker from outside the
registered set. This is the process of verification. When the speaker is not required to
provide an utterance of a specific text, the task is called Open-Set, Text-Independent
Speaker Identification (OSTI-SI). In the literature, it is acknowledged that OSTI-SI is
the most challenging class of speaker recognition [3-4]. A factor influencing the
complexity of OSTI-SI is the size of the population of registered speakers. In theory,
as this population grows, the confusion in discriminating amongst the registered
speakers is likely to increase and therefore the number of incorrect identifications is
likely to increase as well. The growth in the said population also increases the
difficulty in confidently declaring a test utterance as not belonging to any of the
registered speakers, when this is indeed the case. The reason is that, as the population
size grows, the possibility of a voice originating from an unknown speaker being very
close to one of the registered speaker models increases. The problem of OSTI-SI is
further complicated by undesired variation in speech characteristics due to anomalous
events. These anomalies can have different forms ranging from the communication
channel and environmental noise to uncharacteristic sounds generated by the
speakers. The resultant variation in speech causes a mismatch between the
corresponding test and pre-stored voice patterns. This can in turn lead to degradation
of the OSTI-SI performance.
Conventionally, the evaluation of OSTI-SI performance has been based on separate
representations of the identification and verification effectiveness. However, for the
purpose of comparing the performance of different systems, it is thought to be
beneficial to consider a measure of performance for the complete process.
2 Evaluation Methodology
Figure 1 summarises the process of open-set, text-independent speaker
identification (OSTI-SI). As shown in this figure, the given test utterance is assigned
to the speaker model that yields the maximum similarity over all speaker models in
the system, if this maximum likelihood score itself is greater than the threshold.
Otherwise, it is declared as originated from a non-registered speaker. It is evident
from the above description and Figure 1 that three types of error are possible in this
process. These, which collectively define the conventional approach to evaluating the
performance of OSTI-SI, are described as follows.
A test utterance from a specific registered speaker, showing its highest
similarity to the reference model for another registered speaker.
Assigning the test utterance to one of the speaker models in the registered set
when it does not belong to any of them.
Declaring the test utterance, which belongs to one of the registered speakers,
as originated from a non-registered speaker.
Performance Evaluation in Open-Set Speaker Identification 3
For the purpose of this paper, these types of error are referred to as OSIE, OSI-FA
and OSI-FR respectively (where OSI, E, FA, and FR stand for open-set identification,
error, false acceptance, and false rejection respectively).
Fig.1. Overview of the open-set, text-independent speaker identification process
It is clear that the identification process is responsible for generating OSIE
whereas, both OSI-FA and OSI-FR are the consequences of the decisions made in the
verification process. It should be noted that an OSIE in the first stage would always
lead to an error regardless of the decision in the second stage. Therefore, in evaluating
the performance in the verification stage, it is important to discard the false speaker
nominations received from the first stage (when the actual speakers are within the
As indicated earlier, an alternative approach to evaluating OSTI-SI is that based on
observing the complete performance of the system. For this purpose, the operations
involved in OSTI-SI are considered hidden in a box as shown in Figure 2. The system
input is a test utterance and the output can either be a decision giving the identity of a
speaker or a decision declaring that the test utterance does not belong to any of the
registered speakers (shown as Unknown).
Fig. 2. Proposed basis for the evaluation of OSTI-SI
λ λ λ λ1
λ λ λ λ2
λ λ λ λ3
λ λ λ λN
Registered Speaker Models
4 A. Malegaonkar, A. Ariyaeeinia
With such a configuration, three types of error can be recorded for a given
threshold as follows.
• A test utterance from a registered speaker is associated with an incorrect
• A test utterance from a registered speaker is declared to have been produced
by an unknown speaker.
• A test utterance from an unknown speaker is associated with a registered
In this study, the above errors are referred to as Mislabelling (ML), False Rejection
(FR) and False Acceptance (FA) respectively.
In order to obtain the overall performance of OSTI-SI, a measure for combining all
the possible types of errors is required. Motivated by the method used for calculating
the diarisation error rate , an appropriate measure that can be proposed for this
purpose is that of Accumulative Error Rate (AER). This is expressed as
where ς is the adopted threshold, T is the total number of tests, and X(ς ) is the
number of decision errors of type X for the adopted threshold ς . It should be noted
that all three error types identified in this methodology, and hence AER are dependent
on the decision threshold. Therefore, if required, equation (1) provides a means for
setting the threshold such that the total error in OSTI-SI is minimised.
3 Experimental Investigations
This section details the experimental work conducted in order to further analyse the
characteristics of the proposed evaluation methodology for OSTI-SI.
3.1 Speech Data
The speech data adopted for this investigation is based on the dataset used for the
1-speaker detection task of NIST SRE 2003 database. The protocol used in this work
is based on that devised in . The overall configuration of this dataset is given in
Performance Evaluation in Open-Set Speaker Identification 5
3.2 Speech Features and Speaker Representation
Each speech frame of 20ms duration is subjected to pre-emphasis and then
analysed to extract a 12th order linear predictive coding-derived cepstral (LPCC)
feature vector at a rate of 10ms. The static features are mean normalised. The first
derivative parameters are also adopted and are based on the polynomial fit over 15
frames. These parameters are appended to the static features.
In this work, each registered speaker is represented by an adapted Gaussian
Mixture Model (GMM) with 1024 components. For this purpose, a gender
independent universal background model (UBM) is first obtained by pooling two
gender dependant UBMs. The models for the registered speakers are then obtained
using a single step adaptation of the gender-independent universal background model
Table 1. Configuration of the dataset
Speakers for Universal
UBM Data Length
4.8 hrs 3.3 hrs
3.3 Results and Discussions
The results of this study in terms of ML, FR, FA, and AER as a function of the
threshold are presented in Figure 3. In this figure, MLR, FAR and FRR are the rates
of ML, FA and FR errors respectively. As observed in this figure, ML and FA errors
decrease by increasing the threshold whereas FR error shows an increasing trend with
an increase in the threshold. Variation in AER shows an interesting trend. This curve
shows a distinct point of minima which is referred to as the point of Minimum-AER
(M-AER). This point represents minimal total incorrect decisions in OSTI-SI. Hence
this point can be an appropriate basis for setting the system threshold for OSTI-SI.
Moreover, this measure is useful in comparing the performance of alternative OSTI-
SI systems. It can also be observed that the largest component of errors at M-AER
point is FR and the increase in FR is associated with reduction in ML decisions.
As discussed earlier, the individual processes of identification and verification in
OSTI-SI are responsible for generating the overall decision errors in OSTI-SI. In
addition to observing the overall performance of these processes, the analysis of the
individual processes is certainly useful for understanding the limitations of the
techniques used in implementing these processes. This is further useful for developing
suitable techniques in order to improve the performance of either of the two specific
processes, and hence OSTI-SI.