A large-scale open-source acoustic simulator for speaker recognition

A large-scale open-source acoustic simulator for speaker recognition
A large-scale open-source acoustic simulator for
speaker recognition
Marc Ferràs, Srikanth Madikeri, Petr Motlicek, Subhadeep Dey and Hervé Bourlard
Idiap Research Institute, CH-1920 Martigny, Switzerland
Abstract—State-of-the-art speaker recognition systems suffer
from significant performance loss on degraded speech conditions
and acoustic mismatch between enrolment and test phases. Past
international evaluation campaigns, such as the NIST Speaker
Recognition Evaluation (SRE), have partly addressed these challenges in some evaluation conditions. This work aims at further
assessing and compensating for the effect of a wide variety
of speech degradation processes on speaker recognition performance. We present an open-source simulator generating degraded telephone, VoIP and interview speech recordings using a
comprehensive list of narrow-band, wide-band and audio codecs,
together with a database of over 60 hours of environmental noise
recordings and over one hundred impulse responses collected
from publicly available data. We provide speaker verification
results obtained with an i-vector based system using either a
clean or degraded PLDA back-end on a NIST SRE subset of
data corrupted by the proposed simulator. While error rates
increase considerably under degraded speech conditions, large
relative EER reductions were observed when using a PLDA model
trained with a large number of degraded sessions per speaker.
Index Terms—speaker recognition, degraded speech, codec,
noise, robustness, simulation
A major challenge for speech technologies is to preserve a
satisfactory performance in diverse environmental and recording conditions. For recognition tasks such as speech recognition or speaker recognition, error rates are known to rapidly
grow as soon as acoustic mismatch between train/enrolment
and test phases is present. Even in matched conditions, error
rates are known to be high when speech signals are simply
degraded. A number of approaches, at the signal or feature
levels [1]–[5] or at the modeling level [6], [7], have been proposed to improve robustness of speaker recognition systems.
Factors such as environmental noise, reverberation, recording equipment or speech coding noise, amongst others, influence the quality of speech signals. While some of these factors
may be controllable in some applications, the massive amount
of available audio data on public archives such as YouTube,
makes addressing robustness an even more challenging problem.
Past international speaker recognition evaluations, e.g. NIST
Speaker Recognition Evaluation (SRE) 2005, 2006 and 2008
have highlighted the sensitivity of state-of-the-art systems to
acoustic channel mismatch [8]. Researchers have proposed
effective session compensation and domain adaptation techniques such as Joint Factor Analysis (JFA) [9] and Probabilistic Linear Discriminant Analysis (PLDA) [10], to deal
Copyright (c) 2015 IEEE. Personal use of this material is permitted.
However, permission to use this material for any other purposes must be
obtained from the IEEE by sending a request to [email protected]
This work was supported by the European Union under the FP7 Integrated
Project SIIP (Speaker Identification Integrated Project). The authors gratefully
thank the EU for their financial support and all project partners for a fruitful
with train/test mismatch with in-domain data. However, PLDA
models have been shown to be sensitive to in-domain/outof-domain mismatch between development and evaluation
data with close conditions such as landline/cellular telephone
speech resulting in a considerable increase in error rates [11].
The presence of environmental noise is also known to
be a major source of degradation and it has been partially
addressed in the latest NIST SRE 2012 [12]. This evaluation
proposed assessing speaker recognition system performance
on noisy speech data, corrupted with Heating, Ventilation and
Air Conditioning (HVAC) noise as well as artificial crowd
noise. Although participants [13], [14] developed systems to
face these specific noisy conditions, significant performance
loss was observed for noisy trials. In an unconstrained lowSNR noisy scenario, the error rate increase in the presence of
noise may be dramatic enough to render a system unreliable.
Some techniques aiming at augmenting the intra-speaker
variability of speech data have been explored for speech recognition in recent years. Vocal Tract Length Perturbation [15],
augmenting data using random VTL Normalization factors,
obtained significant improvements on the TIMIT data. Similar
gains were reported for a stochastic feature mapping technique [16] synthesizing content based on speaker adaptation
techniques. Speed changes were explored in [17], obtaining
gains around 5% relative for several LVCSR tasks.
In this paper, we explore the augmentation of inter-session
variability of a data set using an acoustic simulator for
speaker recognition tasks. We evaluate a state-of-the-art ivector speaker recognition system in the presence of both
environmental noise, reverberation and telephone, radio and
audio codecs. While the data provided in the NIST SRE has
traditionally focused on sampling speaker variability, this work
uses a database aimed at sampling degradation variability
using a large number of simulated degradation processes
while keeping a manageable number of speakers. For this
purpose, we have developed an open-source simulator [18]
using over 60 hours of noise data, twelve speech/audio codecs
covering telephony, VoIP and audio applications and over a
hundred impulse responses of simulations of answering machine, telephone, playback and loudspeaker devices. Train/test
trial lists from the NIST SRE 2010 evaluation campaign are
also provided in the same package to make performance
comparison possible across systems. We provide experimental
results for i-vector systems using clean and degraded speech
PLDA backends. The latter are trained using a development
database with a large number of degraded speech recordings,
following the pooled PLDA approach in [19], proposed for
noise compensation. Further PLDA training approaches were
explored in [20], with tied and pooled PLDA outperforming
per-condition PLDA training in noisy scenarios.
While other simulation initiatives have proposed specific
data sets in the past, e.g. PRISM [21] and QUT [22] data sets,
these tend to use a relatively small number of degradation
processes, in the tens of noise recordings and few impulse
responses. This work goes beyond by publicly releasing a
software package addressing large-scale simulation capable of
generating thousands of acoustic variations of a single clean
speech recording by varying the generation parameters. We
believe these are valuable resources for system development
and evaluation of speech processing algorithms, speaker recognition in particular.
The paper is organized as follows. Section II describes
the proposed acoustic environment simulator. In Section III,
the speech database used for system evaluation is presented.
Section IV gives details about the speaker recognition systems
being evaluated. Experiment details and results are given in
Section V and Section VI gives conclusions about this study.
Collected noise database types, along with the number of files, hours, and
average length per file.
Noise type
Number of files
In this work, a simulator is used as an affordable way of
generating a number of degraded variants of a given clean
speech signal. Three types of non-linear degradation processes
are considered, namely additive noise, telephone and audio
codecs, and room and device impulse responses. Figure 1
shows a simplified block diagram of the simulator. An input
speech signal, recorded in a quiet environment using highquality equipment, is added a noise recording simulating
environmental noise prior to applying reverberation and/or a
speech codec setup. This simulation approach to generating
data, for either evaluation or training, has been used in the past,
e.g. in [21], [23], [24], but at small scale. In this work, we aim
at obtaining a large number of degradation processes, made by
combining the above blocks, to thoroughly sample the acoustic
variability for the same speech sounds of a recording. This
is expected to prevent machine learning paradigms to overfit
while improving their generalization ability. Clean, noise and
impulse response audio is sampled and processed at 16kHz
sample rate.
The noise files are randomly chosen from a 60-hour
database of 1 to 8 minute long recordings collected from
the Freesound online audio archiving site [25]. Around 1400
files1 were downloaded using the Freesound API and were
tagged manually into noise conditions based on the context in
which they were apparently recorded. We consider a total of 7
categories, namely outdoors, public, private and transportation
settings as well as babble, music and impulsive ambience
noises. Table I gives details about the noise database used
in this work. These were recorded by users of the Freesound
website using all sorts of unknown equipment probably ranging from smartphones to professional equipment. The noise
files are added to the clean speech files at levels -15dB lower
than the clean signal level.
The package also provides simulation of linear distortion
produced by devices and rooms. Over 100 impulse responses,
74 for devices and 54 for rooms, were collected from public sources on the internet under licensing allowing for its
redistribution. Impulse responses of smartphones, answering
machines, loudspeakers and microphones were included for
device simulation whereas small, medium and large room
responses were included for simulation of enclosed spaces.
For the impulse responses collected online, mainly by private
and individual initiative, the exact impulse response estimation
methods used are not known. Still, given the amount of time
and effort required for the collection of such data, we believe
these are priceless resources for system development. A set of
12 speech and audio codecs is also included in the acoustic
simulator. These comprise the ITU G.712, P341, IRS and
mIRS telephony band-pass filters for narrow-band telephony
codecs as well as the following lossy codec groups:
• Landline includes mu/A-law companding, following the
ITU G.711 standard for 64 kbps rates. It also includes
Adaptive-Differential PCM (ADPCM) coding following
the ITU G.726 standard, allowing for 16, 32, 48 and
64 kbps rates. The latter is used in international trunks of
the telephone network and in DECT wireless phones.
• Cellular includes two major cellular telephony codecs in
Europe2 , namely the Global System for Mobile Communications (GSM) and narrow-band and wide-band
Advance Multi-rate (AMR-NB and AMR-WB) codecs.
The full-rate specification of GSM (GSM-FR) allowing
for 13 kbps rate uses linear prediction coding with regular pulse excitation. AMR-NB is a multi-rate speech
codec using Algebraic Code-Excited Linear Prediction
(ACELP) at 4.75-12.2 kbps. AMR-WB, following the
ITU G.722.2 specification, is the wide-band variant of
AMR, coding speech signals up to 7 kHz using bit-rates
from 6.6 to 23.8 kbps.
• Satellite/Radio includes three codecs that are used in
satellite and radio telecommunication systems. The ITU
G.728 is a standard using Low-Delay CELP (LD-CELP)
and vector-quantized excitation at 16 kbps. The Continuously Variable Slope Delta (CVSD) modulation is a 1-bit
sample vocoder using an adaptive quantization step. Bitrates range from 12 kbps, common for radio and military
phones, to 64 kbps, used for wireless headset to mobile
phone communication. CVSD has been formerly used for
satellite communications as well. Codec2 is a low bit-rate
codec that is patent-free and open source. Codec2 is able
to encode speech at 0.7-3.2 kbps via sinusoidal modeling
of speech. It is mainly used in radio communications.
• Voice over IP includes the ITU G.729 and ITU G.728
standards besides SILK and SILK-WB, former Skype
now open-source codecs. ITU G.729a is a narrow-band
low-complexity codec based on the Code-Excited variant
of ACELP (CS-ACELP), operating at 8 kbps. The ITU
G.722 is a wide-band audio codec based on sub-band
ADPCM allowing 48, 56 and 64 kbps rates. It is used for
1 Freesound recordings are made available under Creative Commons License
Attributions allowing their use for research purposes.
2 For copyright reasons, EVRC codecs used in the U.S. are not allowed to
be used in the context of this work.
Noise DB
7 scenarios
taken from the microphone data of the NIST SRE 2010 data.
The simulator was run on each of the train and test data
sets using five codec conditions versus a clean and a noisy
condition at 15dB SNR, with non-overlapping noise files being
used for the development, train and test data sets. Codecs and
room impulse responses were available for training the noisy
PLDA backend. This experimental setup results in a total of 20
degraded conditions including clean and noisy experiments.
HVAC noise, room IR,
Fig. 1. Simplified block diagram of the environment simulator. A first module
adds real noise recordings from a large noise database. The second module
further degrades speech using telephone and interview conditions.
voice over IP and radio broadcasters. The SILK codec is
based on linear prediction and it can adapt its bandwidth
from 8 to 24 kHz and quality from 6 to 40 kbps.
• Audio includes the Fraunhofer MPEG Layer III (MP3)
and Advanced Audio Codec (AAC) codecs, both being
developed for general audio and music. AAC has a more
efficient and simpler filter-bank based on the Modified
Discrete Cosine Transform (MDCT) and better coding
of stationary and transient signals compared to MP3.
These codecs are used in many consumer smartphones
and audio recorders.
The acoustic simulator is released as a package available
online [18]. It includes scripts to download and process
noise and impulse response data together with the speech
audio codecs. Although many degradation processes can be
generated, five conditions were fixed, targeting typical speaker
recognition scenarios: landline, cellular, satellite, voip and
interview on clean and 15dB noisy conditions. For Interviews,
HVAC noise, small room reverberation and audio codecs were
For each of these conditions, codec parameters such as bitrate, dtx or packet loss as well as noise recordings and impulse
responses are randomly sampled. The simulator includes a
reliable random number generator to ensure the reproducibility
of the degradation processes across different sites.
While the motivation in using an environment simulator is to
have the ability to generate a large number of degraded speech
files from a single one, the amount of generated data becomes
rapidly intractable if both speaker and degradation variabilities
are densely sampled. In this work, we emphasize on sampling
the degradation processes while keeping a reasonable number
of speech segments and speakers. This suggests targeting an
operating point in the DET curve [26] that is likely to be
populated with scores, e.g. the Equal Error Rate (EER) as opposed to the minimum Decision Cost Function (DCF) used in
the NIST SRE Evaluations. As an alternative quality measure,
Mean Opinion Score - Listening Quality Objective (MOSLQO) is assessed using the PESQ algorithm [27]. MOS-LQO
scores for speech segments with at least 0.5 s leading nonspeech and minimum duration of 3.2 s were averaged.
The training and test data involve 361 utterances and
speakers and 644 utterances and 361 speakers, respectively,
For the speaker verification experiments, we use two Kaldibased [28] i-vector systems, featuring a standard i-vector
extractor, using either clean or degraded PLDA backends.
A. I-vector Frontend
We use 20 Mel-Frequency Cepstral Coefficients (MFCC)
computed every 10ms along with delta and double delta features prior to short-term Gaussianization using a 3 s window.
The 2048-mixture UBM and the total variability matrix of the
i-vector extractor are trained using Fisher English Part I and
II data. The dimension of the i-vectors is 400.
B. Clean PLDA Backend
We use LDA to improve the discrimination of speaker ivectors followed by PLDA scoring. Both LDA and PLDA
parameters are trained using the NIST data sets SRE 2004,
2005, 2006, 2008 and 2008 extended and Swithboard Part II
and Part III. Details about this system are given in [29].
C. Degraded PLDA Backend
The PLDA hyperparameters of this system are trained using
degraded i-vectors from a development data set taken from the
NIST SRE 2010 microphone data, from the remaining speakers not included in the train and test sets. This is simulated data
involving 13 sessions per speaker in average, for 95 speakers.
Each session is degraded using 10 random degradation processes, one per condition as defined in Section II. This makes
up a total of around 12,000 i-vectors for PLDA training. Note
that, for each session of each speaker, 10 degraded variations
from the same exact recording are used. This is expected to
help PLDA in separating speaker and session effects, as only
variation related to channel and noise is considered. Almost
all the possible parameter variations were randomly sampled
for the generation of this data set, except SNR which was
either clean or 15dB. Sampled parameters include band-pass
telephone filters (G.712, P341, IRS, mIRS), codec choice, bitrates (codec dependant), packet loss probabilities (from 0 to
10%), signal input level (from -26dB to -35dB), noise files
and room impulse responses. Uniform distributions were used
to sample these parameters.
We performed two sets of experiments involving 10 experiments each. The first set of experiments assessed the performance of an i-vector system using a clean PLDA backend.
The second set assess the performance of an i-vector system
using a PLDA backend trained on a development data set
involving degraded speech data generated using the simulator.
These experiments were compared to a baseline system using
a clean backend evaluated on clean speech data, downsampled
MOS-LQO scores, ranging from 1 (bad) to 5 (excellent), for each condition
in the evaluation data. Rows denote codec conditions and columns denote
noise conditions.
EER (%) for an i-vector speaker recognition system using clean (first and
second columns) and degraded (third and foruth colummns) PLDA backends.
Rows denote codec conditions and columns denote noise conditions.
MOS-LQO (1-5)
Codec Condition
EER (%)
from 16 kHz to 8 kHz to match the sample rate of the degraded
data. The EER for this system is 1.8%.
Table II gives speech quality assessment scores obtained
using the PESQ algorithm. Systems are ranked consistently
across clean and noisy conditions, with codec conditions being
ranked as landline, cellular, voip, satellite and interview. Satellite shows a 16% decrease of MOS score relative to the worst
landline, cellular or voip scores (from 3.7 to 3.1 for clean,
3.0 to 2.5). This condition uses very low-complexity codecs
such as CVSD and low bit-rate codecs such as Codec2, using
sinusoidal analysis-synthesis, both resulting in a decrease in
speech quality that is captured by PESQ. The scores for the
interview condition are the worst of all five conditions with
scores as low as 1.6. Such low scores are probably due to the
time smearing introduced by the room impulse responses, only
present in the interview condition. However, it must be noted
that reverberant speech is out of the scope of PESQ [30].
The two first columns of Table III shows EERs for speaker
recognition systems using a PLDA backend trained using clean
data. Error rates mostly follow the trends shown in the speech
quality experiments of Table II, with landline, cellular and voip
conditions obtaining considerably lower error rates than satellite and interview. In any case, from 1.8% EER for the baseline
system, the best performing system achieves 3.3% EER, a 83%
relative increase. This is attributed to the mismatch resulting
from applying the simulated degratation processes. Although
the PLDA backend has been trained using telephone speech
from the NIST SRE data sets, large EER increases were found
for telephone speech codecs such as landline or cellular (4.5%
and 6.8%, respectively, compared to 1.8%). This indicates
strong mismatch as well for simulated and real telephone
channels. For satellite and interview conditions, error rates rise
dramatically, reaching over 20% EER. These results suggest a
severe mismatch between development and train/test data, and
highlight the sensitivity of speaker recognition systems to it.
For noisy test conditions (second column of Table III), EER
become even larger, with the best performing system, landline,
achieving 6.0% EER from the 1.8% obtained by the baseline
system, more than three times lower error rate. EER increase
is large for landline, cellular and voip, the best performing
conditions, while satellite and interview conditions achieve
less dramatic performance losses of 10% and 18% in relative
terms, respectively (19.2 to 21.1 and 21.8 to 25.8).
A set of speaker recognition experiments were conducted
to evaluate the potential of using a large development data
set with multiple variants of degraded speech recordings. All
the session recordings from the remaining 130 speakers from
the NIST SRE 2010 data, around 13 sessions per speaker in
average, were degraded using each of the 10 conditions used in
the simulator, resulting in around 13,000 utterances. The noise
Clean PLDA
Codec Condition
No codec
Degraded PLDA
files come from a disjoint data set while the same codecs, in its
different variants, and impulse responses were used for train,
test and development sets. A new PLDA model was trained
using these data.
The third and fourth columns of Table III give EER for
the system using the degraded PLDA backend. A minor EER
increase was found for the baseline condition, from 1.8%
to 1.9%. For all degraded conditions, large improvements
in EER, six out of ten conditions with over 40% relative
EER decrease, were achieved. On one side, this shows that
PLDA alone, trained with a large number of sessions per
speaker - 12 sessions times 10 degraded versions, is able to
considerably improve system performance when test channels
have been seen during PLDA training. Therefore, this error
reduction can not be solely attributed to data augmentation,
but to matching development and train/test channels as well.
For noisy experiments, train/test noise was not observed at
development time. On the other side, the simulated variants
of each session are generated from the same clean recording,
thus feeding PLDA training with pure channel/noise variation
rather than feeding a conglomerate of aggregate factors such
as content or channel from real data. This might facilitate the
discrimination of speakers in the i-vector space using PLDA.
An open-source simulator capable of generating a large
number of degradation variants from clean speech data was introduced. This simulator uses a large noise recording database
covering several ambiences, over a hundred impulse responses
for rooms and audio playback devices and telephone speech,
voice IP, satellite, radio communication and audio codecs.
The simulator has been used to generate ten conditions targeting scenarios of interest for speaker recognition. I-vector
systems using clean and degraded data for PLDA backend
were evaluated. A minimum relative increase in EER of
80% was found when evaluating an i-vector system with a
clean PLDA backend on degraded data, whereas error rates
were found to be up to 10 times larger for interview data
compared to the baseline condition. Retraining the PLDA
backend using simulated development data with around 120
sessions per speaker reduced error rates by 40% relative for
six out of the ten conditions. These results serve only as a
demonstration of the data augmentation package, and more
careful experimentation is required to assess the full potential
of data augmentation for speaker recognition, especially under
channel mismatched conditions between development, train
and test data.
[1] R. Martin, “Noise power spectral density estimation based on optimal
smoothing and minimum statistics,” in IEEE Trans. on Speech and Audio
Processing, 2001.
[2] N. Fakotakis T. Ganchev, I. Potamitis and G. Kokkinaki, “Textindependent speaker verification for real fast-varying noisy environments,” Speech Communication, p. 281292, 2004.
[3] Q. Wu and L. Zhang, “Auditory sparse representation for robust speaker
recognition based on tensor structure,” in EURASIP Journal of Audio
Speech and Music Processing, 2008.
[4] J. Sandberg T. Kinnunen, R. Saeidi and M. Hansson-Sandsten, “What
else is new than the hamming window? robust mfccs for speaker
recognition via multitapering,” in Proc. INTERSPEECH, 2010.
[5] S. Thomas S. Ganapathy and H. Hermansky, “Feature extraction using
2-d autoregressive models for speaker recognition,” in Proc. of the IEEE
Speaker Odyssey Workshop, 2012.
[6] L. Burget Y. Lei and N. Scheffer, “A noise robust i-vector extractor using
vector taylor series for speaker recognition,” in Proc. IEEE ICASSP,
[7] N. Scheffer L. Ferrer M. McLaren, Y. Lei, “Application of convolutional
neural networks to speaker recognition in noisy conditions,” in Proc.
[8] “Nist speaker recognition evaluation results 2005, 2006, 2008 and 2010,”
http://www.nist.gov/itl/iad/mig/sre.cfm, Accessed November 2015.
[9] P. Kenny, G. Boulianne, P. Ouellet, and P. Dumouchel, “Joint factor
analysis versus eigenchannels in speaker recognition,” IEEE Trans. on
Audio, Speech and Language Processing, vol. 16, no. 5, pp. 980–988,
[10] S. Ioffe, “Probabilistic linear discriminant analysis,” in ECCV, 2006,
pp. 531–542.
[11] Daniel Garcia-Romero and Alan McCree, “Supervised domain adaptation for i-vector based speaker recognition,” in Proc. IEEE ICASSP,
2014, pp. 4047–4051.
[12] “Nist speaker recognition evaluation 2012,” http://www.nist.gov/itl/iad/
mig/sre12.cfm, Accessed March 25th, 2015.
[13] G. Liu N. Shokouhi H. Boril T. Hasan, S. O. Sadjadi and J. H. Hansen,
“Crss systems for 2012 nist speaker recognition evaluation,” in Proc.
[14] N. Scheffer Y. Lei M. Graciarena L. Ferrer, M. McLaren and V. Mitra,
“A noise-robust system for nist 2012 speaker recognition evaluation,” in
Proc. INTERSPEECH, 2013.
[15] Navdeep Jaitly and Geoffrey E Hinton, “Vocal tract length perturbation
(vtlp) improves speech recognition,” in Proc. ICML Workshop on Deep
Learning for Audio, Speech and Language, 2013.
[16] Xiaodong Cui, Vikas Goel, and Brian Kingsbury, “Data augmentation
for deep neural network acoustic modeling,” in Proc. IEEE ICASSP,
2014, pp. 5582–5586.
[17] Tom Ko, Vijayaditya Peddinti, Daniel Povey, and Sanjeev Khudanpur,
“Audio augmentation for speech recognition,” in Proc. INTERSPEECH,
[18] “Idiap acoustic simulator,” http://github.com/idiap/acoustic-simulator,
Accessed November 2015.
[19] L. Ferrer M. Graciarena Y. Lei, L. Burget and N. Scheffer, “Towards
noise-robust speaker recognition using probabilistic linear discriminant
analysis,” in Proc. IEEE ICASSP, 2012, pp. 4253–4256.
[20] D. Garcia-Romero and C. Y. Espy-Wilson, “Multicondition training of
gaussian plda models in i-vector space for noise and reverberation robust
speaker recognition,” in Proc. IEEE ICASSP, 2012.
[21] L. Ferrer, H. Bratt, L. Burget, H. Cernocky, O. Glembek, M. Graciarena,
A. Lawson, Y. Lei, P. Matejka, O. Plchot, et al., “Promoting robustness
for speaker modeling in the community: the prism evaluation set,” in
Proceedings of NIST 2011 Workshop, 2011.
[22] David B Dean, Ahilan Kanagasundaram, Houman Ghaemmaghami,
Md Hafizur Rahman, and Sridha Sridharan, “The qut-noise-sre protocol
for the evaluation of noisy speaker recognition,” 2015.
[23] H. G. Hirsch and D. Pearce, “The AURORA Experimental Framework
for the Performance Evaluations of Speech Recognition Systems under
Noisy Condition,” in ISCA ITRW ASR2000 Automatic Speech Recognition: Challenges for the Next Millennium, France, 2000.
[24] E. Khoury, L. E. Shaffey, and S. Marcel, “The Idiap Speaker Recognition
Evaluation System at NIST SRE 2012,” in NIST Speaker Recognition
Conference, Orlando, USA, 2012.
[25] “Freesound.org,” http://freesound.org, Accessed March 25th, 2015.
[26] A. Martin, G. Doddington, T. Kamm, M. Ordowski, and M. Przybocki,
“The det curve in assessment of detection task performance,” in Proc.
EUROSPEECH, 1997, vol. 4, pp. 1895–1898.
[27] Antony W Rix, John G Beerends, Michael P Hollier, and Andries P
Hekstra, “Perceptual evaluation of speech quality (pesq)-a new method
for speech quality assessment of telephone networks and codecs,” in
Proc. IEEE ICASSP, 2001, vol. 2, pp. 749–752.
[28] Daniel Povey, Arnab Ghoshal, Gilles Boulianne, Lukáš Burget, Ondřej
Glembek, Nagendra Goel, Mirko Hannemann, Petr Motlı́ček, Yanmin
Qian, Petr Schwarz, et al., “The kaldi speech recognition toolkit,” 2011.
[29] Petr Motlicek et al., “Employment of subspace gaussian mixture models
in speaker recognition,” in To Appear In Proc. of ICASSP 2015, 2015.
[30] P.862.3, Application guide for objective quality measurement based on
Recommendations P.862, P.862.1 and P.862.2, 2007.
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