Original: SpeakerGAN: Recognizing Speakers in New Languages with Generative Adversarial Networks
Revised: SpeakerGAN: Recognizing Speakers in New Languages with Generative Adversarial Networks

Original: Abstract
Revised: Abstract

Original: Verifying a person’s identity based on their voice is a challenging, real-worldproblem in biometric security.
Revised: Verifying a person’s identity based on their voice is a challenging, real-worldproblem in biometric security.

Original: A crucial requirement of such speaker veriﬁcationsystems is to be domain robust.
Revised: A crucial requirement of such speaker veriﬁcationsystems is to be domain robust.

Original: Performance should not degrade even if speakersare talking in languages not seen during training.
Revised: Performance should not degrade even if speakersare talking in languages not seen during training.

Original: To this end, we present a ﬂexibleand interpretable framework for learning domain invariant speaker embeddingsusing Generative Adversarial Networks.
Revised: To this end, we present a ﬂexibleand interpretable framework for learning domain invariant speaker embeddingsusing Generative Adversarial Networks.

Original: We combine adversarial training withan angular margin loss function, which encourages the speaker embedding modelto be discriminative by directly optimizing for cosine similarity between classes.
Revised: We combine adversarial training withan angular margin loss function, which encourages the speaker embedding modelto be discriminative by directly optimizing for cosine similarity between classes.

Original: We are able to beat a strong baseline system using a cosine distance classiﬁerand a simple score-averaging strategy.
Revised: We are able to beat a strong baseline system using a cosine distance classiﬁerand a simple score-averaging strategy.

Original: Our results also show that models withadversarial adaptation perform signiﬁcantly better than unadapted models.
Revised: Our results also show that models withadversarial adaptation perform signiﬁcantly better than unadapted models.

Original: In anattempt to better understand this behavior, we quantitatively measure the degree ofinvariance induced by our proposed methods using Maximum Mean Discrepancyand Fréchet distances.
Revised: In anattempt to better understand this behavior, we quantitatively measure the degree ofinvariance induced by our proposed methods using Maximum Mean Discrepancyand Fréchet distances.

Original: Our analysis shows that our proposed adversarial speakerembedding models signiﬁcantly reduce the distance between source and target datadistributions, while performing similarly on the former and better on the latter.
Revised: Our analysis shows that our proposed adversarial speakerembedding models signiﬁcantly reduce the distance between source and target datadistributions, while performing similarly on the former and better on the latter.

Original:  Introduction
Revised:  Introduction

Original: Text-Independent Speaker Veriﬁcation remains a challenging problem in the domain of biometricsecurity.
Revised: Text-Independent Speaker Veriﬁcation remains a challenging problem in the domain of biometricsecurity.

Original: Armed with the machinery of deep learning, veriﬁcation systems can now be deployedin the wild and are still capable of delivering robust performance.
Revised: Armed with the machinery of deep learning, veriﬁcation systems can now be deployedin the wild and are still capable of delivering robust performance.

Original: In the veriﬁcation community,situations wherein the test data is signiﬁcantly different from the data available during system trainingare referred to as - In the Wild.
Revised: In the veriﬁcation community,situations wherein the test data is signiﬁcantly different from the data available during system trainingare referred to as - In the Wild.

Original: For instance, the NIST-SRE 2016 evaluation data contains Cantoneseand Tagalog speakers (in-domain, target data), while most of the speakers in our training set aretalking in English (out-of-domain, source data).
Revised: For instance, the NIST-SRE 2016 evaluation data contains Cantoneseand Tagalog speakers (in-domain, target data), while most of the speakers in our training set aretalking in English (out-of-domain, source data).

Original: This distribution shift or mismatch between trainingand test data is an obstacle in several areas of pattern recognition and machine learning [1], and leadsto a degradation in system performance.
Revised: This distribution shift or mismatch between trainingand test data is an obstacle in several areas of pattern recognition and machine learning [1], and leadsto a degradation in system performance.

Original: The development biometric veriﬁcation system that performreliably in such conditions is critical for this technology be used safely and securely on a day-to-daybasis.
Revised: The development biometric veriﬁcation system that performreliably in such conditions is critical for this technology be used safely and securely on a day-to-daybasis.

Original: Deep neural networks (DNN) have revolutionized several areas of speech processing, and as such,are ideal candidates for learning discriminative speaker representations or embeddings [19, 9, 22, 3].
Revised: Deep neural networks (DNN) have revolutionized several areas of speech processing, and as such,are ideal candidates for learning discriminative speaker representations or embeddings [19, 9, 22, 3].

Original: Indeed, neural speaker embeddings have surpassed the performance of i-vectors [19, 5], especiallyon real world, in the wild data [16, 13].Arguably the most popular approach for learning speakerembeddings is to optimize the parameters of a DNN by minimizing the cross-entropy loss overspeakers in the training data.
Revised: Indeed, neural speaker embeddings have surpassed the performance of i-vectors [19, 5], especiallyon real world, in the wild data [16, 13].Arguably the most popular approach for learning speakerembeddings is to optimize the parameters of a DNN by minimizing the cross-entropy loss overspeakers in the training data.

Original: Cross-entropy is natural choice for identifying speakers, however itdoes not directly address the veriﬁcation task.
Revised: Cross-entropy is natural choice for identifying speakers, however itdoes not directly address the veriﬁcation task.

Original: As a consequence of not being optimized ‘end-toend’, the performance of cross-entropy speaker embeddings (X-vectors) is heavily dependent on a powerful classiﬁer to perform veriﬁcation.
Revised: As a consequence of not being optimized ‘end-toend’, the performance of cross-entropy speaker embeddings (X-vectors) is heavily dependent on a powerful classiﬁer to perform veriﬁcation.

Original: This dependence on a classiﬁer motivates the research anddevelopment of end-to-end systems.
Revised: This dependence on a classiﬁer motivates the research anddevelopment of end-to-end systems.

Original: We also believe that such systems can also beneﬁt in downstreamtasks that make use of speaker embeddings, such as speech recognition and synthesis.
Revised: We also believe that such systems can also beneﬁt in downstreamtasks that make use of speaker embeddings, such as speech recognition and synthesis.

Original: Speakerveriﬁcation is a challenging problem, and modern veriﬁcation datasets like NIST-SRE 2016, addto this challenge by introducing a mismatch between the distributions of the training and test data.
Revised: Speakerveriﬁcation is a challenging problem, and modern veriﬁcation datasets like NIST-SRE 2016, addto this challenge by introducing a mismatch between the distributions of the training and test data.

Original: This phenomena is referred to as domain or covariate shift.
Revised: This phenomena is referred to as domain or covariate shift.

Original: In the case of NIST-SRE 2016, the testdata consists of Cantonese and Tagalog speakers, whereas the vast majority of training speakers aretalking in English.
Revised: In the case of NIST-SRE 2016, the testdata consists of Cantonese and Tagalog speakers, whereas the vast majority of training speakers aretalking in English.

Original: NIST also provide a small amount of unlabelled, in-domain, target data, that canbe used to compensate for the domain shift.
Revised: NIST also provide a small amount of unlabelled, in-domain, target data, that canbe used to compensate for the domain shift.

Original: Most the domain adaptation techniques that have beenproposed for speaker veriﬁcation have been proposed on top of i-vectors or x-vectors.
Revised: Most the domain adaptation techniques that have beenproposed for speaker veriﬁcation have been proposed on top of i-vectors or x-vectors.

Original: In this work we present a framework for learning domain invariant speaker embeddings using
Revised: In this work we present a framework for learning domain invariant speaker embeddings using

Original: Generative Adversarial Networks (GAN).
Revised: Generative Adversarial Networks (GAN).

Original: We drawn inspiration from research in computer vision,where GAN based unsupervised domain adaptation methods have been extremely successful [20,17, 18], and adapt these ideas for feature learning in a veriﬁcation setting.
Revised: We drawn inspiration from research in computer vision,where GAN based unsupervised domain adaptation methods have been extremely successful [20,17, 18], and adapt these ideas for feature learning in a veriﬁcation setting.

Original: The basic idea is castdomain adaptation/invariance as an adversarial game - generate features or embeddings such that adiscriminator cannot tell if they come from the source or target domain.
Revised: The basic idea is castdomain adaptation/invariance as an adversarial game - generate features or embeddings such that adiscriminator cannot tell if they come from the source or target domain.

Original: Unlike traditional GANs thatwork in high-dimensional spaces (e.g. natural images,speech), domain adaptation GANs operate inlow-dimensional embedding space.
Revised: Unlike traditional GANs thatwork in high-dimensional spaces (e.g. natural images,speech), domain adaptation GANs operate inlow-dimensional embedding space.

Original: We extend our recent work [2, 4] and propose a novel objectivefor updating the generator network.
Revised: We extend our recent work [2, 4] and propose a novel objectivefor updating the generator network.

Original: We ﬁnd that optimizing GAN models with this objective provesto be unstable, and propose to stabilize it by augmenting the discriminator with an auxiliary lossfunction.
Revised: We ﬁnd that optimizing GAN models with this objective provesto be unstable, and propose to stabilize it by augmenting the discriminator with an auxiliary lossfunction.

Original: This strategy also helped stabilize training for the conventional generator objective but wasnot strictly needed.
Revised: This strategy also helped stabilize training for the conventional generator objective but wasnot strictly needed.

Original: Additionally, we analyze the transformed source and target data distributions in order to gain furtherinsight regarding the performance of our method.
Revised: Additionally, we analyze the transformed source and target data distributions in order to gain furtherinsight regarding the performance of our method.

Original: We measure distances between these distributionsusing Maximum Mean Discrepancy and Fréchet distances.
Revised: We measure distances between these distributionsusing Maximum Mean Discrepancy and Fréchet distances.

Original: From our analysis we see a that goodperformance in terms of distributional distance corresponds to good veriﬁcation performance.
Revised: From our analysis we see a that goodperformance in terms of distributional distance corresponds to good veriﬁcation performance.

Original: Ourspeaker veriﬁcation experiments show that the proposed adversarial speaker embedding frameworkdelivers robust performance, signiﬁcantly outperforming a strong i-vector baseline.
Revised: Ourspeaker veriﬁcation experiments show that the proposed adversarial speaker embedding frameworkdelivers robust performance, signiﬁcantly outperforming a strong i-vector baseline.

Original: Furthermore, byaveraging the scores of our different GAN models, we are able to achieve state-of-the-art results.
Revised: Furthermore, byaveraging the scores of our different GAN models, we are able to achieve state-of-the-art results.

Original:  Models2.1 Feature Extractor (Generator)
Revised:  Models2.1 Feature Extractor (Generator)

Original: The ﬁrst step for learning discriminative speaker embeddings is to learn a mapping F ( X s ) −→ f ,f ∈ R D from a sequence of speech frames from speaker s to a D-dimensional feature vector f .
Revised: The ﬁrst step for learning discriminative speaker embeddings is to learn a mapping F ( X s ) −→ f ,f ∈ R D from a sequence of speech frames from speaker s to a D-dimensional feature vector f .

Original: F ( X )can be implemented using a variety of neural network architectures.
Revised: F ( X )can be implemented using a variety of neural network architectures.

Original: We design or feature extractorusing a residual network structure.
Revised: We design or feature extractorusing a residual network structure.

Original: We choose to model speech using 1-dimensional convolutionalﬁlters, owing to the fact that speech is translation invariant along the time-axis only.
Revised: We choose to model speech using 1-dimensional convolutionalﬁlters, owing to the fact that speech is translation invariant along the time-axis only.

Original: Following theresidual blocks we use a combination of self-attention and dense layers in order to represent inputaudio of arbitrary size by a ﬁxed-size vector, f .
Revised: Following theresidual blocks we use a combination of self-attention and dense layers in order to represent inputaudio of arbitrary size by a ﬁxed-size vector, f .

Original: Unlike traditional approaches, our proposed featureextractor is updated with an adversarial loss in addition to the standard task loss.
Revised: Unlike traditional approaches, our proposed featureextractor is updated with an adversarial loss in addition to the standard task loss.

Original: 2.2 Self-Attentive Speaker Statistics
Revised: 2.2 Self-Attentive Speaker Statistics

Original: Self-Attention models are an active area of research in the speaker veriﬁcation community.
Revised: Self-Attention models are an active area of research in the speaker veriﬁcation community.

Original: Intuitively,such models allow the network to focus on fragments of speech that are more speaker discriminative.
Revised: Intuitively,such models allow the network to focus on fragments of speech that are more speaker discriminative.

Original: The attention layers computes a scalar weight corresponding to each time-step t :
Revised: The attention layers computes a scalar weight corresponding to each time-step t :

Original: [Equation]
Revised: [Equation]

Original: [Equation]
Revised: [Equation]

Original: These weights are then normalized, α t = softmax ( e t ) , to give them a probabilistic interpretation.
Revised: These weights are then normalized, α t = softmax ( e t ) , to give them a probabilistic interpretation.

Original: We use the attention model proposed in [24], which extends attention to the mean as well as standarddeviation:
Revised: We use the attention model proposed in [24], which extends attention to the mean as well as standarddeviation:

Original: [Equation]
Revised: [Equation]

Original: t (2)
Revised: t (2)

Original: 1x1,3x1,1x1,1x1, 256 3x1,1x1,1x1,3x1,1x1,1x1,3x1,1x1,xxxx
Revised: 1x1,3x1,1x1,1x1, 256 3x1,1x1,1x1,3x1,1x1,1x1,3x1,1x1,xxxx

Original: AttentiveStatistics
Revised: AttentiveStatistics

Original: FC
 FC
Revised: FC
 FC

Original: [Figure]
Revised: [Figure]

Original: Residual Blocks
Revised: Residual Blocks

Original: Conv1 3x23,
Revised: Conv1 3x23,

Original: [Figure]
Revised: [Figure]

Original: Feature Extractor
Revised: Feature Extractor

Original: Speaker Classiﬁer
Revised: Speaker Classiﬁer

Original: [Figure]
Revised: [Figure]

Original: BCE
Revised: BCE

Original: [Equation]
Revised: [Equation]

Original: In this work we apply the use of self attention to convolutional feature maps, as indicated in Fig.
Revised: In this work we apply the use of self attention to convolutional feature maps, as indicated in Fig.

Original: The last residual block outputs a tensor of size nBXnFxT , where nB is the batch size, nF is thenumber of ﬁlters and T is time.
Revised: The last residual block outputs a tensor of size nBXnFxT , where nB is the batch size, nF is thenumber of ﬁlters and T is time.

Original: The input to the attention layer, h t , is a nF dimensional vector.
Revised: The input to the attention layer, h t , is a nF dimensional vector.

Original: By using a self-attention model, we also equip our network with a more robust framework forprocessing inputs of arbitrary size than simple global averaging.
Revised: By using a self-attention model, we also equip our network with a more robust framework forprocessing inputs of arbitrary size than simple global averaging.

Original: This allows us simply forwardpropagate a recording through the network in order to extract speaker embeddings.
Revised: This allows us simply forwardpropagate a recording through the network in order to extract speaker embeddings.

Original: 2.3 Classiﬁer
Revised: 2.3 Classiﬁer

Original: The classiﬁer block, C ( f , θ y ) , is arguably the key component of the model, as it is responsible forlearning speaker discriminative features.
Revised: The classiﬁer block, C ( f , θ y ) , is arguably the key component of the model, as it is responsible forlearning speaker discriminative features.

Original: Recently, angular margin loss functions have been proposedas an alternative to contrastive loss functions for veriﬁcation tasks [10, 23].
Revised: Recently, angular margin loss functions have been proposedas an alternative to contrastive loss functions for veriﬁcation tasks [10, 23].

Original: The Additive Marginsoftmax (AM-softmax) loss function is one such algorithm with an intuitive interpretation.
Revised: The Additive Marginsoftmax (AM-softmax) loss function is one such algorithm with an intuitive interpretation.

Original: The losscomputes similarity between classes using cosine, and forces the similarity of the correct class to begreater than that of incorrect classes by a margin m .
Revised: The losscomputes similarity between classes using cosine, and forces the similarity of the correct class to begreater than that of incorrect classes by a margin m .

Original: [Equation]
Revised: [Equation]

Original: Where W T and f i are the normalized weight vector and speaker embedding respectively.
Revised: Where W T and f i are the normalized weight vector and speaker embedding respectively.

Original: The
Revised: The

Original: AM-softmax loss also adds a scale parameter s , which helps the model converge faster.
Revised: AM-softmax loss also adds a scale parameter s , which helps the model converge faster.

Original: We selectm = 0 .
Revised: We selectm = 0 .

Original: 6 and s = 30 for all our experiments.
Revised: 6 and s = 30 for all our experiments.

Original: 2.4 Domain Discriminator
Revised: 2.4 Domain Discriminator

Original: The domain discriminator D ( . ) is tasked with determining if embeddings come from the sourceor target domains, and is arguably the most important component of the model.
Revised: The domain discriminator D ( . ) is tasked with determining if embeddings come from the sourceor target domains, and is arguably the most important component of the model.

Original: In order to learndomain invariant features, we engage the domain discriminator in an adversarial game with the feaureextractor E ( . ) .
Revised: In order to learndomain invariant features, we engage the domain discriminator in an adversarial game with the feaureextractor E ( . ) .

Original: The domain discriminator consists of two fully connected layers followed by theoutput layer.
Revised: The domain discriminator consists of two fully connected layers followed by theoutput layer.

Original:  Domain Adversarial Speaker Embeddings
Revised:  Domain Adversarial Speaker Embeddings

Original: A crucial requirement for learning speaker embeddings that are domain invariant is to ﬁnd a balancebetween the task loss and the adversarial loss.
Revised: A crucial requirement for learning speaker embeddings that are domain invariant is to ﬁnd a balancebetween the task loss and the adversarial loss.

Original: The objective to learn a feature space whereinembeddings are speaker discriminative irrespective of the domain.
Revised: The objective to learn a feature space whereinembeddings are speaker discriminative irrespective of the domain.

Original: Key to achieving this is the domaindiscriminator D , which is trained using the Binary Cross-Entropy loss (BCE).
Revised: Key to achieving this is the domaindiscriminator D , which is trained using the Binary Cross-Entropy loss (BCE).

Original: [Equation]
Revised: [Equation]

Original: Where X s , X t represent source and target data respectively.
Revised: Where X s , X t represent source and target data respectively.

Original: E ( . ) is the feature extractor/generator.
Revised: E ( . ) is the feature extractor/generator.

Original: The adversarial game between D ( . ) and E ( . ) is given by:
Revised: The adversarial game between D ( . ) and E ( . ) is given by:

Original: [Equation]
Revised: [Equation]

Original: Equation (3) represents the most general form of the GAN game, and can be used to representdifferent adversarial frameworks depending on the choice of L advE
Revised: Equation (3) represents the most general form of the GAN game, and can be used to representdifferent adversarial frameworks depending on the choice of L advE

Original: Gradient Reversal: We obtain the gradient reversal framework by setting L advE = −L advD .
Revised: Gradient Reversal: We obtain the gradient reversal framework by setting L advE = −L advD .

Original: Gradient reversal optimizes the true minmax objective of the adversarial game.
Revised: Gradient reversal optimizes the true minmax objective of the adversarial game.

Original: However, thisobjective can become problematic, since the discriminator converges early during training and leadsto vanishing gradients.
Revised: However, thisobjective can become problematic, since the discriminator converges early during training and leadsto vanishing gradients.

Original: GAN: Rather than directly using the minimax loss, the standard way to train the generator is usingthe inverted label loss.
Revised: GAN: Rather than directly using the minimax loss, the standard way to train the generator is usingthe inverted label loss.

Original: The generator objective is given by:
Revised: The generator objective is given by:

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: This splits the optimization into two independent objectives, one for the generator and one for thediscriminator.
Revised: This splits the optimization into two independent objectives, one for the generator and one for thediscriminator.

Original: This loss has the same ﬁxed-point properties as the minimax loss while providingstronger gradients to target mappings [20].
Revised: This loss has the same ﬁxed-point properties as the minimax loss while providingstronger gradients to target mappings [20].

Original: 3.1 Updating the Generator with Source Embeddings
Revised: 3.1 Updating the Generator with Source Embeddings

Original: In a typical GAN setting, the generator is trained only using fake data (with inverted labels).
Revised: In a typical GAN setting, the generator is trained only using fake data (with inverted labels).

Original: Thisstructure is also maintained in several adversarial domain adaptation algorithms.
Revised: Thisstructure is also maintained in several adversarial domain adaptation algorithms.

Original: However, in thecontext of this work we believe that updating the generator using both source and target data can bebeneﬁcial.
Revised: However, in thecontext of this work we believe that updating the generator using both source and target data can bebeneﬁcial.

Original: In this case, the generator loss simply inverts the discriminator loss of eq.
Revised: In this case, the generator loss simply inverts the discriminator loss of eq.

Original: [Equation]
Revised: [Equation]

Original: When using the proposed objective for training the generator, we are optimizing the true minimaxloss like in the gradient reversal approach.
Revised: When using the proposed objective for training the generator, we are optimizing the true minimaxloss like in the gradient reversal approach.

Original: Unfortunately, we found that optimizing this loss becomesunstable early during training.
Revised: Unfortunately, we found that optimizing this loss becomesunstable early during training.

Original: We found a simple approach to stabilize training for this model was toaugment the discriminator with an auxiliary loss function.
Revised: We found a simple approach to stabilize training for this model was toaugment the discriminator with an auxiliary loss function.

Original: 3.2 Auxiliary Classiﬁer GAN
Revised: 3.2 Auxiliary Classiﬁer GAN

Original: The Auxiliary Classiﬁer GAN (AuxGAN) model augments the standard GAN framework with anauxiliary loss to perform conditional image generation [15].
Revised: The Auxiliary Classiﬁer GAN (AuxGAN) model augments the standard GAN framework with anauxiliary loss to perform conditional image generation [15].

Original: This approach aims to predict sideinformation (such as class labels), as opposed to feed the same information to the generator and discriminator.
Revised: This approach aims to predict sideinformation (such as class labels), as opposed to feed the same information to the generator and discriminator.

Original: In the context of this work, our goal is to use the prediction loss for regularization andrepresentation learning.
Revised: In the context of this work, our goal is to use the prediction loss for regularization andrepresentation learning.

Original: [Equation]
Revised: [Equation]

Original: Eq.
Revised: Eq.

Original: (9) is a modiﬁed version of the AuxGAN objective.
Revised: (9) is a modiﬁed version of the AuxGAN objective.

Original: In particular, the original formulation alsouses the auxiliary loss to train the generator as well (with fake data being assigned its own uniquelabel).
Revised: In particular, the original formulation alsouses the auxiliary loss to train the generator as well (with fake data being assigned its own uniquelabel).

Original: We found that the auxiliary loss was crucial for stabilizing L adv E ( X s , X t , D ) when usingthe formulation in eq.
Revised: We found that the auxiliary loss was crucial for stabilizing L adv E ( X s , X t , D ) when usingthe formulation in eq.

Original: In our experiments we found that the AuxGAN setup stabilizes modeltraining even when we use eq.
Revised: In our experiments we found that the AuxGAN setup stabilizes modeltraining even when we use eq.

Original: (7) as the generator objective, and leads to slightly better veriﬁcationperformance.
Revised: (7) as the generator objective, and leads to slightly better veriﬁcationperformance.

Original: In this setting only the discriminator is trained with the auxillary loss.
Revised: In this setting only the discriminator is trained with the auxillary loss.

Original: 3.3 GAN Variants
Revised: 3.3 GAN Variants

Original: Since their introduction, GANs have been one of the most researched topics in the deep learningcommunity.
Revised: Since their introduction, GANs have been one of the most researched topics in the deep learningcommunity.

Original: Several variations of the original formulation have been proposed, each with differentgenerative characteristics and stability issues.
Revised: Several variations of the original formulation have been proposed, each with differentgenerative characteristics and stability issues.

Original: In this work we explore three GAN variants in additionto the standard GAN - Least-Squares GAN [12], Auxiliary Classiﬁer GAN and Relativistic GAN [8].
Revised: In this work we explore three GAN variants in additionto the standard GAN - Least-Squares GAN [12], Auxiliary Classiﬁer GAN and Relativistic GAN [8].

Original: We use the standard average GAN variant of the Relativistic GAN model.
Revised: We use the standard average GAN variant of the Relativistic GAN model.

Original: These models differ in thestructure of the discriminator network.
Revised: These models differ in thestructure of the discriminator network.

Original: We show that each variant transforms the feature space indifferent way, will all the model showing mostly similar performance.
Revised: We show that each variant transforms the feature space indifferent way, will all the model showing mostly similar performance.

Original: Additionally we see that byfusing the performance of all GAN variants together through score averaging we achieve the bestoverall performance.
Revised: Additionally we see that byfusing the performance of all GAN variants together through score averaging we achieve the bestoverall performance.

Original:  Experimental Setup
Revised:  Experimental Setup

Original: Training Data (Source): We used audio from previous NIST-SRE evaluations (2004-2010) and
Revised: Training Data (Source): We used audio from previous NIST-SRE evaluations (2004-2010) and

Original: Switchboard Cellular audio for training the proposed DANSE model as well as the x-vector andi-vector baseline systems.
Revised: Switchboard Cellular audio for training the proposed DANSE model as well as the x-vector andi-vector baseline systems.

Original: We also augment our data with noise and reverberation, as in [].
Revised: We also augment our data with noise and reverberation, as in [].

Original: We add128k noisy copies to the clean speech, ending up with ˜ 220k recordings in our training set.
Revised: We add128k noisy copies to the clean speech, ending up with ˜ 220k recordings in our training set.

Original: For DANSEmodel training we ﬁlter out speakers with less than 5 recordings, ending up with approximately6000 speakers, whereas the x-vector and i-vector systems were trained using the Kaldi recipe.
Revised: For DANSEmodel training we ﬁlter out speakers with less than 5 recordings, ending up with approximately6000 speakers, whereas the x-vector and i-vector systems were trained using the Kaldi recipe.

Original: Wenote that the vast majority of our training data consists of English speakers, and is recorded overtelephone/cellular channels.
Revised: Wenote that the vast majority of our training data consists of English speakers, and is recorded overtelephone/cellular channels.

Original: Model: The Embedding function/Generator , E ( . ) , consists of a 3 X 23 input convolutional layer, 4residual blocks [3,4,6,3], an attentive statistics layer and two fully connected layers (512,512).
Revised: Model: The Embedding function/Generator , E ( . ) , consists of a 3 X 23 input convolutional layer, 4residual blocks [3,4,6,3], an attentive statistics layer and two fully connected layers (512,512).

Original: Theclassiﬁer , C ( . ) , module consists of a fully connected layer (64) and the AM-softmax output layer.
Revised: Theclassiﬁer , C ( . ) , module consists of a fully connected layer (64) and the AM-softmax output layer.

Original: The former is the ﬁnal domain invariant speaker embedding extracted during evaluation.
Revised: The former is the ﬁnal domain invariant speaker embedding extracted during evaluation.

Original: Finally,the domain discriminator module consists of two fully connected layers (256,256) and a binarycross-entropy output layer.
Revised: Finally,the domain discriminator module consists of two fully connected layers (256,256) and a binarycross-entropy output layer.

Original: Exponential Linear Units (ELU) are used as non-linear activations for alllayers of the network.
Revised: Exponential Linear Units (ELU) are used as non-linear activations for alllayers of the network.

Original: Batch Normalization is used on all layers expect the attentive statistics layer.
Revised: Batch Normalization is used on all layers expect the attentive statistics layer.

Original: Optimization: We start by pre-training the Embedding function using standard cross-entropytraining.
Revised: Optimization: We start by pre-training the Embedding function using standard cross-entropytraining.

Original: Pre-training is carried out using the RMSprop optimizer with a learning rate (lr) of 0 .
Revised: Pre-training is carried out using the RMSprop optimizer with a learning rate (lr) of 0 .

Original: For training GAN based speaker embedding models we use different optimizers for training thethree networks (Embedding function,Classiﬁer, Discriminator).
Revised: For training GAN based speaker embedding models we use different optimizers for training thethree networks (Embedding function,Classiﬁer, Discriminator).

Original: The classiﬁer is optimized using
Revised: The classiﬁer is optimized using

Original: RMSprop with lr = 0 .
Revised: RMSprop with lr = 0 .

Original: 003 , while the domain classiﬁer and feature extractor are trained using SGDwith lr = 0 .
Revised: 003 , while the domain classiﬁer and feature extractor are trained using SGDwith lr = 0 .

Original: We were able to train all our GAN models using the same set of hyper-parameters.
Revised: We were able to train all our GAN models using the same set of hyper-parameters.

Original: We used performance on held out validation set to determine when to stop training.
Revised: We used performance on held out validation set to determine when to stop training.

Original: Data Sampling:
Revised: Data Sampling:

Original: We use an extremely simple approach for sampling data during training.
Revised: We use an extremely simple approach for sampling data during training.

Original: Wesample random chunks of audio (3-8 seconds) from each recording in the training set.
Revised: Wesample random chunks of audio (3-8 seconds) from each recording in the training set.

Original: We sampleeach recording 10 times to deﬁne an epoch.
Revised: We sampleeach recording 10 times to deﬁne an epoch.

Original: For each mini-batch of source data, we randomly sample(with repetition) a mini-batch from the unlabelled adaptation data for GAN training.
Revised: For each mini-batch of source data, we randomly sample(with repetition) a mini-batch from the unlabelled adaptation data for GAN training.

Original: Speaker Veriﬁcation:
Revised: Speaker Veriﬁcation:

Original: At test time we discard the domain discriminator branch of the model, as itis not needed for extracting embeddings.
Revised: At test time we discard the domain discriminator branch of the model, as itis not needed for extracting embeddings.

Original: Extraction is done by performing a forward pass on thefull recording, and using the 64-dimensional fc 3 layer as our speaker embeddings.
Revised: Extraction is done by performing a forward pass on thefull recording, and using the 64-dimensional fc 3 layer as our speaker embeddings.

Original: Veriﬁcation trialsare scored using cosine distance.
Revised: Veriﬁcation trialsare scored using cosine distance.

Original: Veriﬁcation performance is reported in terms of Equal Error Rate(EER).
Revised: Veriﬁcation performance is reported in terms of Equal Error Rate(EER).

Original:  Results
Revised:  Results

Original: NIST-SRE 2016:
Revised: NIST-SRE 2016:

Original: Unlike previous years, The 2016 edition of the NIST-SRE introduced a challengingnew dataset containing Cantonese and Tagalog speakers.
Revised: Unlike previous years, The 2016 edition of the NIST-SRE introduced a challengingnew dataset containing Cantonese and Tagalog speakers.

Original: We use the Kaldi recipes for our baselinei-vector and x-vector systems.
Revised: We use the Kaldi recipes for our baselinei-vector and x-vector systems.

Original: We note that the x-vector baseline may be considered as state-of-the-artperformance on this dataset.
Revised: We note that the x-vector baseline may be considered as state-of-the-artperformance on this dataset.

Original: Adaptation Data (Target): 2722 unlabelled, target data recordings are provided to adapt veriﬁcationsystems.
Revised: Adaptation Data (Target): 2722 unlabelled, target data recordings are provided to adapt veriﬁcationsystems.

Original: [Table] [Table]
Revised: [Table] [Table]

Original: Tables 1 & 2. compares the performance of the different speaker representations on the NIST-SRE16task.
Revised: Tables 1 & 2. compares the performance of the different speaker representations on the NIST-SRE16task.

Original: Among the baseline systems the x-vector model produces the best results, however requires
Revised: Among the baseline systems the x-vector model produces the best results, however requires

Original: LDA based dimensionality reduction and the PLDA classiﬁer to produce its best result.
Revised: LDA based dimensionality reduction and the PLDA classiﬁer to produce its best result.

Original: We seethat all of the GAN based models outperform gradient reversal by a large margin, but none of theindividual models are able to match the best x-vector system.
Revised: We seethat all of the GAN based models outperform gradient reversal by a large margin, but none of theindividual models are able to match the best x-vector system.

Original: Interestingly, we ﬁnd that we are ableto best this system by simply averaging the scores of our different GAN models.
Revised: Interestingly, we ﬁnd that we are ableto best this system by simply averaging the scores of our different GAN models.

Original: The FuseGANresults do not include the scores from the standard GAN model, although this does not affect the ﬁnalperformance signiﬁcantly.
Revised: The FuseGANresults do not include the scores from the standard GAN model, although this does not affect the ﬁnalperformance signiﬁcantly.

Original:  Analysis
Revised:  Analysis

Original: One particularly interesting result from our experiments is the improvement we see through a simplescore averaging procedure.
Revised: One particularly interesting result from our experiments is the improvement we see through a simplescore averaging procedure.

Original: Our hypothesis is that the different discriminator objectives encourage thegenerator to cover different modes of the target data distribution.
Revised: Our hypothesis is that the different discriminator objectives encourage thegenerator to cover different modes of the target data distribution.

Original: This ﬁnding is consistent with GANapproaches that train multiple discriminators [14], although we do not train them simultaneously.
Revised: This ﬁnding is consistent with GANapproaches that train multiple discriminators [14], although we do not train them simultaneously.

Original: In
Revised: In

Original: Fig.
Revised: Fig.

Original: 2 we visualize the embedding spaces learned by our models using t-SNE [11].
Revised: 2 we visualize the embedding spaces learned by our models using t-SNE [11].

Original: While Gradient
Revised: While Gradient

Original: Reversal primarily appears to rotate the feature space, the transformations induced by the GAN models is more pronounced.
Revised: Reversal primarily appears to rotate the feature space, the transformations induced by the GAN models is more pronounced.

Original: Crucially, we see that that the source domain speaker clusters appear toremain intact.
Revised: Crucially, we see that that the source domain speaker clusters appear toremain intact.

Original: This indicates that our models retain discriminative properties in the source domain, afact we verify experimentally.
Revised: This indicates that our models retain discriminative properties in the source domain, afact we verify experimentally.

Original: Maximum Mean Discrepancy (MMD) : is based on the idea that two distributions are identical ifand only if all their moments are identical [6].
Revised: Maximum Mean Discrepancy (MMD) : is based on the idea that two distributions are identical ifand only if all their moments are identical [6].

Original: A divergence can be deﬁned if we can measure how“different” the moments of the two distributions are.
Revised: A divergence can be deﬁned if we can measure how“different” the moments of the two distributions are.

Original: MMD is a method of efﬁciently doing this viathe kernel trick:
Revised: MMD is a method of efﬁciently doing this viathe kernel trick:

Original: [Equation]
Revised: [Equation]

Original: In order to quantitatively evaluate our models in terms of domain adaptation, we measure the
Revised: In order to quantitatively evaluate our models in terms of domain adaptation, we measure the

Original: Maximum Mean Discrepancy distance between a selection of source data and the unlabelled targetdata.
Revised: Maximum Mean Discrepancy distance between a selection of source data and the unlabelled targetdata.

Original: MMD is a standard distribution distance metric and has been applied in the context of domainadaptation [21].
Revised: MMD is a standard distribution distance metric and has been applied in the context of domainadaptation [21].

Original: Fréchet Distance: The Fréchet Inception Distance (ﬁd) is a popular approach for evaluating GANs,and has been shown to correlate well with human judgement of visual quality [7].
Revised: Fréchet Distance: The Fréchet Inception Distance (ﬁd) is a popular approach for evaluating GANs,and has been shown to correlate well with human judgement of visual quality [7].

Original: Instead of an
Revised: Instead of an

Original: Inception network, we extract embeddings from our gan models from the source and target data.
Revised: Inception network, we extract embeddings from our gan models from the source and target data.

Original: The
Revised: The

Original: Fréchet Distance between between the Gaussian ( m s , C s ) obtained from the source data distributionp s and the Gaussian ( m t , C t ) from the target data is given by:
Revised: Fréchet Distance between between the Gaussian ( m s , C s ) obtained from the source data distributionp s and the Gaussian ( m t , C t ) from the target data is given by:

Original: [Equation]
Revised: [Equation]

Original: Source Domain Speaker Veriﬁcation: We use the same source data used to compute the MMD and
Revised: Source Domain Speaker Veriﬁcation: We use the same source data used to compute the MMD and

Original: Fréchet Distance to construct a trial list for veriﬁcation.
Revised: Fréchet Distance to construct a trial list for veriﬁcation.

Original: The list consists of 2500 recordings and wescore them all versus all.
Revised: The list consists of 2500 recordings and wescore them all versus all.

Original: There are a total of 101,666 target and 6,145,834 non-target trials.
Revised: There are a total of 101,666 target and 6,145,834 non-target trials.

Original: From Fig.
Revised: From Fig.

Original: 3 we see that MMD and the Fréchet distance display similar trends.
Revised: 3 we see that MMD and the Fréchet distance display similar trends.

Original: Surprisingly we seethat Gradient Reversal only has a small effect on either metric, while the GAN models all have muchlower MMD and Fr’echet distances.
Revised: Surprisingly we seethat Gradient Reversal only has a small effect on either metric, while the GAN models all have muchlower MMD and Fr’echet distances.

Original: We note that the model using the novel generator objective showsthe lowest scores on both metrics.
Revised: We note that the model using the novel generator objective showsthe lowest scores on both metrics.

Original: The results on source domain speaker veriﬁcation also indicatethat our models remain discriminative in the source domain as well, with only a small degradationas compared to the unadapted model.
Revised: The results on source domain speaker veriﬁcation also indicatethat our models remain discriminative in the source domain as well, with only a small degradation ascompared to the unadapted model.

Original: Interestingly, the Gradient Reversal model shows the bestperformance on  thisexperiment albeit by a small margin.
Revised: The vanilla GAN performs worst on the veriﬁcation task, and this

Original: [Figure]
Revised: [Figure]

Revised: NoAdapt: No Adaptation, GradRev: Gradient Reversal, lsGAN: Least Squares, sGAN: standard, auxGAN: auxiliary classiﬁer, relGAN: Relativistic, auxGAN *:proposed objective relative performance also translates to the target domain.

Revised: Interestingly, the Gradient Reversal modelshows the best performance on this experiment albeit by a small margin.

Original:  Conclusion
Revised:  Conclusion

Original: In this work we we presented a novel framework for learning domain-invariant speaker embeddingsusing GANs.
Revised: In this work we we presented a novel framework for learning domain-invariant speaker embeddingsusing GANs.

Original: By combining a powerful deep feature extractor, an end-to-end loss function andmost importantly, adversarial training we are able to learn extremely compact speaker embeddingsthat deliver robust veriﬁcation performance on challenging evaluation data.
Revised: By combining a powerful deep feature extractor, an end-to-end loss function andmost importantly, adversarial training we are able to learn extremely compact speaker embeddingsthat deliver robust veriﬁcation performance on challenging evaluation data.

Original: We showed that theproposed methods do reduce the domain mismatch between source and target data in terms of MMDand Fréchet distance.
Revised: We showed that theproposed methods do reduce the domain mismatch between source and target data in terms of MMDand Fréchet distance.

Original: Furthermore, we see that our methods adapt while maintaining their speakerdiscriminative nature in the source domain as well.
Revised: Furthermore, we see that our methods adapt while maintaining their speakerdiscriminative nature in the source domain as well.

Original: In future work we will experiment with other
Revised: In future work we will experiment with other

Original: GAN variants in an attempt to further improve performance
Revised: GAN variants in an attempt to further improve performance.