X-VECTORS: ROBUST DNN EMBEDDINGS FOR SPEAKER RECOGNITIONDavid Snyder, Daniel Garcia-Romero, Gregory Sell, Daniel Povey, Sanjeev Khudanpur
Center for Language and Speech Processing & Human Language Technology Center of Excellence
The Johns Hopkins University, Baltimore, MD 21218, USA
ABSTRACTIn this paper, we use data augmentation to improve performance ofdeep neural network (DNN) embeddings for speaker recognition.
The DNN, which is trained to discriminate between speakers, maps
variable-length utterances to fixed-dimensional embeddings that wecall x-vectors. Prior studies have found that embeddings leveragelarge-scale training datasets better than i-vectors. However, it can bechallenging to collect substantial quantities of labeled data for training. We use data augmentation, consisting of added noise and reverberation, as an inexpensive method to multiply the amount of training data and improve robustness. The x-vectors are compared with
i-vector baselines on Speakers in the Wild and NIST SRE 2016 Cantonese. We find that while augmentation is beneficial in the PLDA
classifier, it is not helpful in the i-vector extractor. However, thex-vector DNN effectively exploits data augmentation, due to its supervised training. As a result, the x-vectors achieve superior performance on the evaluation datasets.
Index Terms— speaker recognition, deep neural networks, data
augmentation, x-vectors
1. INTRODUCTION
Using deep neural networks (DNN) to capture speaker characteristics is currently a very active research area. In our approach, representations called x-vectors are extracted from a DNN and used like
i-vectors. This paper builds on our recent DNN embedding architecture [1]. We show that artificially augmenting the training data withnoises and reverberation is a highly effective strategy for improving
performance in DNN embedding systems.
Most speaker recognition systems are based on i-vectors [2].
The standard approach consists of a universal background model(UBM), and a large projection matrix T that are learned in an unsupervised way to maximize the data likelihood. The projection maps
high-dimensional statistics from the UBM into a low-dimensional
representation, known as an i-vector. A probabilistic linear discriminant analysis (PLDA) [3] classifier is used to compare i-vectors, andenable same-or-different speaker decisions [4, 5, 6].
The DNNs most often found in speaker recognition are trained
as acoustic models for automatic speech recognition (ASR), and arethen used to enhance phonetic modeling in the i-vector UBM: either
posteriors from the ASR DNN replace those from a Gaussian mixture model (GMM) [7, 8], or bottleneck features are extracted fromthe DNN and combined with acoustic features [9]. In either case, if
the ASR DNN is trained on in-domain data, the improvement overtraditional acoustic i-vectors is substantial [10, 11, 12]. However,this approach introduces the need for transcribed training data andgreatly increases computational complexity compared to traditional
i-vectors.

Alternatively, neural networks can be directly optimized to discriminate between speakers. This has potential to produce powerful, compact systems [13], that only require speaker labels to train.
In early systems, neural networks are trained to separate speakers,
and frame-level representations are extracted from the network andused as features for Gaussian speaker models [14, 15, 16]. Heigold
et al., introduced an end-to-end system, trained on the phrase “OK
Google,” that jointly learns an embedding along with a similarity
metric to compare pairs of embeddings [13]. Snyder et al., adapted
this approach to a text-independent application and inserted a temporal pooling layer into the network to handle variable-length segments [17]. The work in [1] split the end-to-end approach into twoparts: a DNN to produce embeddings and a separately trained classifier to compare them. This facilitates the use of all the accumulated
backend technology developed over the years for i-vectors, such aslength-normalization, PLDA scoring, and domain adaptation techniques.
DNN embedding performance appears to be highly scalable withthe amount of training data. As a result, these systems have foundsuccess leveraging large proprietary datasets [13, 17, 18]. However,recent systems have shown promising performance trained on only
publicly available speaker recognition corpora [1, 19, 20]. This paper is based on the work in [1] and applies data augmentation to the
DNN training procedure. This increases the amount and diversity ofthe existing training data, and achieves a significant improvement forthe x-vector system. In comparing with x-vectors, we also contributea study of augmentation in i-vector systems.

2. SPEAKER RECOGNITION SYSTEMSThis section describes the speaker recognition systems developed
for this study, which consist of two i-vector baselines and the DNN
x-vector system. All systems are built using the Kaldi speech recognition toolkit [21].

2.1. Acoustic i-vector
A traditional i-vector system based on the GMM-UBM recipe described in [11] serves as our acoustic-feature baseline system. The
features are 20 MFCCs with a frame-length of 25ms that are meannormalized over a sliding window of up to 3 seconds. Delta andacceleration are appended to create 60 dimension feature vectors.
An energy-based speech activity detection (SAD) system selects features corresponding to speech frames. The UBM is a 2048 component full-covariance GMM. The system uses a 600 dimensional
i-vector extractor and PLDA for scoring (see Section 2.4).

2.2. Phonetic bottleneck i-vector
This i-vector system incorporates phonetic bottleneck features(BNF) from an ASR DNN acoustic model and is similar to [9].
The DNN is a time-delay acoustic model with p-norm nonlinearities. The ASR DNN is trained on the Fisher English corpus and uses
the same recipe and architecture as the system described in Section
2.2 of [11], except that the penultimate layer is replaced with a 60
dimensional linear bottleneck layer. Excluding the softmax output
layer, which is not needed to compute BNFs, the DNN has 9.2
million parameters.
The BNFs are concatenated with the same 20 dimensional
MFCCs described in Section 2.1 plus deltas to create 100 dimensional features. The remaining components of the system (featureprocessing, UBM, i-vector extractor, and PLDA classifier) are identical to the acoustic system in Section 2.1.
2.3. The x-vector system
This section describes the x-vector system. It is based on the DNN
embeddings in [1] and described in greater detail there.
Our software framework has been made available in the Kaldi
toolkit. An example recipe is in the main branch of Kaldi at https://github.com/kaldi-asr/kaldi/tree/master/egs/
sre16/v2 and a pretrained x-vector system can be downloaded
from http://kaldi-asr.org/models.html. The recipe
and model are similar to the x-vector system described in Section
4.4.
Layer
Layer context Total context Input x output
frame1[t − 2, t + 2]5
120x512frame2{t − 2, t, t + 2}
91536x512
frame3{t − 3, t, t + 3}
151536x512
frame4{t}
15
512x512frame5{t}
15
512x1500
stats pooling[0, T )
T
1500T x3000
segment6{0}T
3000x512
segment7{0}T
512x512softmax
{0}
T
512xN

Table 1. The embedding DNN architecture. x-vectors are extracted
at layer segment6, before the nonlinearity. The N in the softmax
layer corresponds to the number of training speakers.
The features are 24 dimensional filterbanks with a frame-lengthof 25ms, mean-normalized over a sliding window of up to 3 seconds.
The same energy SAD as used in the baseline systems filters out
nonspeech frames.
The DNN configuration is outlined in Table 1. Suppose an input
segment has T frames. The first five layers operate on speech frames,with a small temporal context centered at the current frame t. For
example, the input to layer frame3 is the spliced output of frame2, atframes t − 3, t and t + 3. This builds on the temporal context of theearlier layers, so that frame3 sees a total context of 15 frames.
The statistics pooling layer aggregates all T frame-level outputs
from layer frame5 and computes its mean and standard deviation.
The statistics are 1500 dimensional vectors, computed once for each
input segment. This process aggregates information across the time
dimension so that subsequent layers operate on the entire segment.
In Table 1, this is denoted by a layer context of {0} and a total context of T . The mean and standard deviation are concatenated to-gether and propagated through segment-level layers and finally thesoftmax output layer. The nonlinearities are all rectified linear units(ReLUs).
The DNN is trained to classify the N speakers in the training
data. A training example consists of a chunk of speech features(about 3 seconds average), and the corresponding speaker label. After training, embeddings are extracted from the affine component oflayer segment6. Excluding the softmax output layer and segment7(because they are not needed after training) there is a total of 4.2
million parameters.
2.4. PLDA classifier
The same type of PLDA [3] classifier is used for the x-vector andi-vector systems. The representations (x-vectors or i-vectors) arecentered, and projected using LDA. The LDA dimension was tunedon the SITW development set to 200 for i-vectors and 150 for
x-vectors. After dimensionality reduction, the representations arelength-normalized and modeled by PLDA. The scores are normalized using adaptive s-norm [22].
3. EXPERIMENTAL SETUP
3.1. Training data
The training data consists of both telephone and microphone speech,
the bulk of which is in English. All wideband audio is downsampled
to 8kHz.
The SWBD portion consists of Switchboard 2 Phases 1, 2, and 3
as well as Switchboard Cellular. In total, the SWBD dataset contains
about 28k recordings from 2.6k speakers. The SRE portion consists of NIST SREs from 2004 to 2010 along with Mixer 6 and contains about 63k recordings from 4.4k speakers. In the experiments
in Sections 4.1–4.4 the extractors (UBM/T or embedding DNN) aretrained on SWBD and SRE and the PLDA classifiers are trained onjust SRE. Data augmentation is described in Section 3.3 and is applied to these datasets as explained throughout Section 4.
In the last experiment in Section 4.5 we incorporate audio fromthe new VoxCeleb dataset [19] into both extractor and PLDA training lists. The dataset consists of videos from 1,251 celebrity speakers. Although SITW and VoxCeleb were collected independently,
we discovered an overlap of 60 speakers between the two datasets.
We removed the overlapping speakers from VoxCeleb prior to using
it for training. This reduces the size of the dataset to 1,191 speakers
and about 20k recordings.
The ASR DNN used in the i-vector (BNF) system was trainedon the Fisher English corpus. To achieve a limited form of domain
adaptation, the development data from SITW and SRE16 is pooled
and used for centering and score normalization. No augmentation isapplied to these lists.
3.2. Evaluation
Our evaluation consists of two distinct datasets: Speakers in the Wild
(SITW) Core [23] and the Cantonese portion of the NIST SRE 2016
evaluation (SRE16) [24]. SITW consists of unconstrained video audio of English speakers, with naturally occurring noises, reverberation, as well as device and codec variability. The SRE16 portion
consists of Cantonese conversational telephone speech. Both enroll and test SITW utterances vary in length form 6–240 seconds.
For SRE16, the enrollment utterances contain about 60 seconds ofspeech while the test utterances vary from 10–60 seconds.

SITW Core
EER(%)

DCF10−2

SRE16 Cantonese
−3

DCF10

EER(%)

DCF10−2

DCF10−3

4.1

Original systems

i-vector (acoustic)
i-vector (BNF)
x-vector

9.29
9.10
9.40

0.621
0.558
0.632

0.785
0.719
0.790

9.23
9.68
8.00

0.568
0.574
0.491

0.741
0.765
0.697

4.2

PLDA aug.

i-vector (acoustic)
i-vector (BNF)
x-vector

8.64
8.00
7.56

0.588
0.514
0.586

0.755
0.689
0.746

8.92
8.82
7.45

0.544
0.532
0.463

0.717
0.726
0.669

4.3

Extractor aug.

i-vector (acoustic)
i-vector (BNF)
x-vector

8.89
7.27
7.19

0.626
0.533
0.535

0.790
0.730
0.719

9.20
8.89
6.29

0.575
0.569
0.428

0.748
0.777
0.626

4.4

PLDA andextractor aug.

i-vector (acoustic)
i-vector (BNF)
x-vector

8.04
6.49
6.00

0.578
0.492
0.488

0.752
0.690
0.677

8.95
8.29
5.86

0.555
0.534
0.410

0.720
0.749
0.593

4.5

Incl. VoxCeleb

i-vector (acoustic)
i-vector (BNF)
x-vector

7.45
6.09
4.16

0.552
0.472
0.393

0.723
0.660
0.606

9.23
8.12
5.71

0.557
0.523
0.399

0.742
0.751
0.569

Table 2. Results using data augmentation in various systems. “Extractor” refers to either the UBM/T or the embedding DNN. For eachexperiment, the best results are boldface.

We report results in terms of equal error-rate (EER) and the minimum of the normalized detection cost function (DCF) at PTarget =10−2 and PTarget = 10−3 . Note that the SRE16 results have notbeen “equalized [24].”
3.3. Data augmentation
Augmentation increases the amount and diversity of the existing
training data. Our strategy employs additive noises and reverberation. Reverberation involves convolving room impulse responses(RIR) with audio. We use the simulated RIRs described by Ko et
al. in [25], and the reverberation itself is performed with the multicondition training tools in the Kaldi ASpIRE recipe [21]. For additive noise, we use the MUSAN dataset, which consists of over 900
noises, 42 hours of music from various genres and 60 hours of speechfrom twelve languages [26]. Both MUSAN and the RIR datasets arefreely available from http://www.openslr.org.
We use a 3-fold augmentation that combines the original “clean”
training list with two augmented copies. To augment a recording, wechoose between one of the following randomly:• babble: Three to seven speakers are randomly picked from
MUSAN speech, summed together, then added to the original
signal (13-20dB SNR).
• music: A single music file is randomly selected from MUSAN, trimmed or repeated as necessary to match duration,
and added to the original signal (5-15dB SNR).
• noise: MUSAN noises are added at one second intervals
throughout the recording (0-15dB SNR).
• reverb: The training recording is artificially reverberated via
convolution with simulated RIRs.
4. RESULTS
The main results are presented in Table 2 and are referenced throughout Sections 4.1–4.5. We compare performance of two i-vector systems, labeled i-vector (acoustic) and i-vector (BNF), with the xvector system. The systems are described in Sections 2.1, 2.2 and
2.3, respectively. Throughout the following sections, we use the term
extractor to refer to either the UBM/T or the embedding DNN.

4.1. Original systems
In this section, we evaluate systems without data augmentation. The
extractors are trained on the SWBD and SRE datasets describedin Section 3.1. The PLDA classifiers are trained on just the SRE
dataset. Without using augmentation, the best results on SITW are
obtained by i-vector (BNF), which is 12% better than the x-vector
system at DCF10−2 . The acoustic i-vector system also achieves
slightly lower error-rates than the x-vector system on SITW. However, even without augmentation, the best results for SRE16 Cantonese are obtained by the x-vectors. In terms of DCF10−2 , theseembeddings are about 14% better than either i-vector system. We
observe that i-vector (BNF) has no advantage over i-vector (acoustic) for this Cantonese speech. This echoes recent studies that havefound that the large gains achieved by BNFs in English speech are
not necessarily transferable to non-English data [27].
4.2. PLDA augmentation
In this experiment, the augmentation strategy described in Section
3.3 is applied to only the PLDA training list. We use the same extractors as the previous section, which were trained on the original
datasets. PLDA augmentation results in a clear improvement for all
three systems relative to Section 4.1. However, it appears that thex-vectors may benefit from the PLDA augmentation more than thebaseline systems. On SITW, the x-vector system achieves slightly
lower error-rates than i-vector (acoustic), but continues to lag behind
i-vector (BNF) at most operating points. On SRE16, the x-vectors
maintain an advantage over the i-vectors by about 14% in DCF10−2 .
4.3. Extractor augmentation
We now apply data augmentation to the extractor (UBM/T or embedding DNN) training lists but not the PLDA list. The effect ofaugmenting the UBM/T is inconsistent in the i-vector system. Thisobservation is supported by prior studies on i-vectors, which havefound that augmentation is only effective in the PLDA classifier[28, 29]. On the other hand, augmenting the embedding DNN training list results in a large improvement. In contrast to the i-vector systems, this is considerably more effective than augmenting the PLDA

4.4. PLDA and extractor augmentation
In the previous sections, we saw that PLDA augmentation was helpful in both i-vector and DNN embedding systems, although extractor
augmentation was only clearly beneficial in the embedding system.
In this experiment, we apply data augmentation to both the extractor
and PLDA training lists. We continue to use SWBD and SRE for
extractor training and only SRE for PLDA. On SITW the x-vectors
are now 10-25% better than i-vector (acoustic) and are slightly better
than i-vector (BNF) at all operating points. On SRE16 Cantonese,the x-vectors continue to maintain the large lead over the i-vector
systems established in Section 4.3.
4.5. Including VoxCeleb

60

Miss probability (in %)40

40

i-vector (acoustic)
i-vector (BNF)
x-vector

20
10
5
2
1
0.5
0.1
0.01

0.1 0.5 1 2
5 10 20
False Alarm probability (in %)40

60

Fig. 2. DET curve for the SITW Core using Section 4.5 systems.

These results are illustrated by detection error tradeoff (DET) plots
in Figures 1 and 2.
i-vector (acoustic)
i-vector (BNF)
x-vector

5. CONCLUSIONS

20
10
5
2
1
0.5
0.1
0.01

60

Miss probability (in %)training list. On SITW, the x-vector system achieves lower errorrates than i-vector (acoustic) and has now caught up to the i-vector(BNF) system. On SRE16, the x-vectors are now 25% better than thei-vectors in DCF10−2 , which is almost double the improvement the
DNN embeddings had with PLDA augmentation alone. The findingsin this section indicate that data augmentation is only beneficial for
extractors trained with supervision.

0.1 0.5 1 2
5 10 20
False Alarm probability (in %)40

60

Fig. 1. DET curve for the Cantonese portion of NIST SRE16 using
Section 4.5 systems.
The training data in Sections 4.1–4.4 is dominated by telephone
speech. In this experiment, we explore the effect of adding a large
amount of microphone speech to the systems in Section 4.4. The
VoxCeleb dataset [19] is augmented, and added to both the extractor
and PLDA lists. As noted in Section 3.1, we found 60 speakers
which overlap with SITW; all speech for these speakers was removed
from the training lists.
On SITW, both i-vector and x-vector systems improve significantly. However, the x-vector exploits the large increase in theamount of in-domain data better than the i-vector systems. Compared to i-vector (acoustic), the x-vectors are better by 44% in EER
and 29% in DCF10−2 . Compared to the i-vector (BNF) system, it is
now better by 32% in EER and 17% in DCF10−2 . On SRE16, thei-vector systems remain roughly the same compared to Section 4.4,
but the x-vectors improve on all operating points by a small amount.

This paper studied DNN embeddings for speaker recognition. We
found that data augmentation is an easily implemented and effective
strategy for improving their performance. We also made the x-vector
system – our implementation of DNN embeddings – available in the
Kaldi toolkit. We found that the x-vector system significantly outperformed two standard i-vector baselines on SRE16 Cantonese. After
including a large amount of augmented microphone speech, the xvectors achieved much lower error-rates than our best baseline on
Speakers in the Wild. Bottleneck features from an ASR DNN areused in our best i-vector system, and so it requires transcribed data
during training. On the other hand, the x-vector DNN needs only
speaker labels to train, making it potentially ideal for domains withlittle transcribed speech. More generally, it appears that x-vectors
are now a strong contender for next-generation representations forspeaker recognition.
6. ACKNOWLEDGMENTS
This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No.
1232825. This work was partially supported by NSF Grant No CRI1513128. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do notnecessarily reflect the views of the National Science Foundation.
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