Published as a conference paper at ICLR 2021

IMPROVING ZERO-SHOT VOICE STYLE TRANSFER VIADISENTANGLED REPRESENTATION LEARNING

Siyang Yuan1∗, Pengyu Cheng1∗, Ruiyi Zhang1, Weituo Hao1, Zhe Gan2 and Lawrence Carin1

1Duke University, Durham, North Carolina, USA2Microsoft, Redmond, Washington, USA{siyang.yuan,pengyu.cheng}@duke.edu

ABSTRACT

Voice style transfer, also called voice conversion, seeks to modify one speaker’svoice to generate speech as if it came from another (target) speaker. Previousworks have made progress on voice conversion with parallel training data andpre-known speakers. However, zero-shot voice style transfer, which learns fromnon-parallel data and generates voices for previously unseen speakers, remainsa challenging problem. We propose a novel zero-shot voice transfer method viadisentangled representation learning. The proposed method first encodes speaker-related style and voice content of each input voice into separated low-dimensionalembedding spaces, and then transfers to a new voice by combining the source con-tent embedding and target style embedding through a decoder. With information-theoretic guidance, the style and content embedding spaces are representative and(ideally) independent of each other. On real-world VCTK datasets, our methodoutperforms other baselines and obtains state-of-the-art results in terms of trans-fer accuracy and voice naturalness for voice style transfer experiments under bothmany-to-many and zero-shot setups.

1 INTRODUCTION

Style transfer, which automatically converts a data instance into a target style, while preservingits content information, has attracted considerable attention in various machine learning domains,including computer vision (Gatys et al., 2016; Luan et al., 2017; Huang & Belongie, 2017), videoprocessing (Huang et al., 2017; Chen et al., 2017), and natural language processing (Shen et al.,2017; Yang et al., 2018; Lample et al., 2019; Cheng et al., 2020b). In speech processing, styletransfer was earlier recognized as voice conversion (VC) (Muda et al., 2010), which converts onespeaker’s utterance, as if it was from another speaker but with the same semantic meaning. Voicestyle transfer (VST) has received long-term research interest, due to its potential for applicationsin security (Sisman et al., 2018), medicine (Nakamura et al., 2006), entertainment (Villavicencio &Bonada, 2010) and education (Mohammadi & Kain, 2017), among others.

Although widely investigated, VST remains challenging when applied to more general applicationscenarios. Most of the traditional VST methods require parallel training data, i.e., paired voicesfrom two speakers uttering the same sentence. This constraint limits the application of such modelsin the real world, where data are often not pair-wise available. Among the few existing modelsthat address non-parallel data (Hsu et al., 2016; Lee & Wu, 2006; Godoy et al., 2011), most methodscannot handle many-to-many transfer (Saito et al., 2018; Kaneko & Kameoka, 2018; Kameoka et al.,2018), which prevents them from converting multiple source voices to multiple target speaker styles.Even among the few non-parallel many-to-many transfer models, to the best of our knowledge, onlytwo models (Qian et al., 2019; Chou & Lee, 2019) allow zero-shot transfer, i.e., conversion from/tonewly-coming speakers (unseen during training) without re-training the model.

The only two zero-shot VST models (AUTOVC (Qian et al., 2019) and AdaIN-VC (Chou & Lee,2019)) share a common weakness. Both methods construct encoder-decoder frameworks, which ex-tract the style and the content information into style and content embeddings, and generate a voicesample by combining a style embedding and a content embedding through the decoder. With thecombination of the source content embedding and the target style embedding, the models generate

∗Equal contribution.

1

Published as a conference paper at ICLR 2021

the transferred voice, based only on source and target voice samples. AUTOVC (Qian et al., 2019)uses a GE2E (Wan et al., 2018) pre-trained style encoder to ensure rich speaker-related informationin style embeddings. However, AUTOVC has no regularizer to guarantee that the content encoderdoes not encode any style information. AdaIN-VC (Chou & Lee, 2019) applies instance normaliza-tion (Ulyanov et al., 2016) to the feature map of content representations, which helps to eliminatethe style information from content embeddings. However, AdaIN-VC fails to prevent content infor-mation from being revealed in the style embeddings. Both methods cannot assure that the style andcontent embeddings are disentangled without information revealed from each other.

With information-theoretic guidance, we propose a disentangled-representation-learning methodto enhance the encoder-decoder zero-shot VST framework, for both style and content informa-tion preservation. We call the proposed method Information-theoretic Disentangled Embeddingfor Voice Conversion (IDE-VC). Our model successfully induces the style and content of voicesinto independent representation spaces by minimizing the mutual information between style andcontent embeddings. We also derive two new multi-group mutual information lower bounds, tofurther improve the representativeness of the latent embeddings. Experiments demonstrate that ourmethod outperforms previous works under both many-to-many and zero-shot transfer setups on twoobjective metrics and two subjective metrics.

2 BACKGROUND

In information theory, mutual information (MI) is a crucial concept that measures the dependencebetween two random variables. Mathematically, the MI between two variables x and y is

I(x;y) := Ep(x,y)[

logp(x,y)

p(x)p(y)

], (1)

where p(x) and p(y) are marginal distributions of x and y, and p(x,y) is the joint distribution.Recently, MI has attracted considerable interest in machine learning as a criterion to minimize ormaximize the dependence between different parts of a model (Chen et al., 2016; Alemi et al., 2016;Hjelm et al., 2018; Velickovic et al., 2018; Song et al., 2019). However, the calculation of exact MIvalues is challenging in practice, since the closed form of joint distribution p(x,y) in equation (1)is generally unknown. To solve this problem, several MI estimators have been proposed. For MImaximization tasks, Nguyen, Wainwright and Jordan (NWJ) (Nguyen et al., 2010) propose a lowerbound by representing (1) as an f -divergence (Moon & Hero, 2014):

INWJ := Ep(x,y)[f(x,y)]− e−1Ep(x)p(y)[ef(x,y)], (2)

with a score function f(x,y). Another widely-used sample-based MI lower bound is In-foNCE (Oord et al., 2018), which is derived with Noise Contrastive Estimation (NCE) (Gutmann &Hyvarinen, 2010). With sample pairs {(xi,yi)}Ni=1 drawn from the joint distribution p(x,y), theInfoNCE lower bound is defined as

INCE := E[ 1

N

N∑i=1

logef(xi,yi)

1N

∑Nj=1 e

f(xi,yj)

]. (3)

For MI minimization tasks, Cheng et al. (2020a) proposed a contrastively learned upper bound thatrequires the conditional distribution p(x|y):

I(x;y) ≤ E[ 1

N

N∑i=1

[log p(xi|yi)−

1

N

N∑j=1

log p(xj |yi)]]. (4)

where the MI is bounded by the log-ratio of conditional distribution p(x|y) between positive andnegative sample pairs. In the following, we derive our information-theoretic disentangled represen-tation learning framework for voice style transfer based on the MI estimators described above.

3 PROPOSED MODEL

We assume access to N audio (voice) recordings from M speakers, where speaker u has Nu voicesamples Xu = {xui}Nu

i=1. The proposed approach encodes each voice input x ∈ X = ∪Mu=1Xuinto a speaker-related (style) embedding s = Es(x) and a content-related embedding c = Ec(x),

2

Published as a conference paper at ICLR 2021

using respectively a style encoder Es(·) and a content encoder Ec(·). To transfer a source xuifrom speaker u to the target style of the voice of speaker v, xvj , we combine the content embed-ding cui = Ec(xui) and the style embedding svj = Es(xvj) to generate the transferred voicexu→v,i = D(svj , cui) with a decoder D(s, c). To implement this two-step transfer process, we in-troduce a novel mutual information (MI)-based learning objective, that induces the style embeddings and content embedding c into independent representation spaces (i.e., ideally, s contains rich styleinformation of xwith no content information, and vice versa). In the following, we first describe ourMI-based training objective in Section 3.1, and then discuss the practical estimation of the objectivein Sections 3.2 and 3.3.

3.1 MI-BASED DISENTANGLING OBJECTIVE

From an information-theoretic perspective, to learn representative latent embedding (s, c), it is de-sirable to maximize the mutual information between the embedding pair (s, c) and the input x.Meanwhile, the style embedding s and the content c are desired to be independent, so that we cancontrol the style transfer process with different style and content attributes. Therefore, we minimizethe mutual information I(s; c) to disentangle the style embedding and content embedding spaces.Consequently, our overall disentangled-representation-learning objective seeks to minimize

L = I(s; c)− I(x; s, c) = I(s; c)− I(x; c|s)− I(x; s). (5)

As discussed in Locatello et al. (Locatello et al., 2019), without inductive bias for supervision, thelearned representation can be meaningless. To address this problem, we use the speaker identity uas a variable with values {1, . . . ,M} to learn representative style embedding s for speaker-relatedattributes. Noting that the process from speaker u to his/her voice xui to the style embedding sui (asu → x → s) is a Markov Chain, we conclude I(s;x) ≥ I(s;u) based on the MI data-processinginequality (Cover & Thomas, 2012) (as stated in the Supplementary Material). Therefore, we replaceI(s;x) in L with I(s;u) and minimize an upper bound instead:

L = I(s; c)− I(x; c|s)− I(u; s) ≥ I(s; c)− I(x; c|s)− I(x; s), (6)

In practice, calculating the MI is challenging, as we typically only have access to samples, and lackthe required distributions (Chen et al., 2016). To solve this problem, below we provide several MIestimates to the objective terms I(s; c), I(x; c|s) and I(u; s).

3.2 MI LOWER BOUND ESTIMATION

To maximize I(u; s), we derive the following multi-group MI lower bound (Theorem 3.1) based onthe NWJ bound developed in Nguyen et al. (Nguyen et al., 2010). The detailed proof is provided inthe Supplementary Material. Let µ(−ui)

v = µv represent the mean of all style embeddings in groupXv , constituting the style centroid of speaker v; µ(−ui)

u is the mean of all style embeddings in groupXu except data point xui, representing a leave-xui-out style centroid of speaker u. Intuitively, weminimize ‖sui − µ(−ui)

u ‖ to encourage the style embedding of voice xui to be more similar to thestyle centroid of speaker u, while maximizing ‖sui − µ(−ui)

v ‖ to enlarge the margin between suiand the other speakers’ style centroids µv . We denote the right-hand side of (7) as I1.

Theorem 3.1. Let µ(−ui)v = 1

Nv

∑Nv

k=1 svk if u 6= v; and µ(−ui)u = 1

Nu−1∑j 6=i suj . Then,

I(u; s) ≥ E[ 1

N

M∑u=1

Nu∑i=1

[− ‖sui − µ(−ui)

u ‖2 − e−1

N

M∑v=1

Nv exp{−‖sui − µ(−ui)v ‖2}

]]. (7)

To maximize I(x; c|s), we derive a conditional mutual information lower bound below:

Theorem 3.2. Assume that given s = su, samples {(xui, cui)}Nui=1 are observed. With a variational

distribution qφ(x|s, c), we have I(x; c|s) ≥ E[I], where

I =1

N

M∑u=1

Nu∑i=1

[log qφ(xui|cui, su)− log

( 1

Nu

Nu∑j=1

qφ(xuj |cui, su))]. (8)

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Published as a conference paper at ICLR 2021

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Figure 1: Training and transfer processes. (a) Training style encoder Es with objective I1: Allvoice samples are encoded into style embedding space. For style embedding sui of xui, we min-imize its distance with speaker u’s style centroid µu, and maximize its distance to other speakerstyle centroids µv . (b) Training for content encoder Ec and decoder D as objectives I2, I3: Weencode content cui from voice xui from speaker u. The style of speaker u is encoded from anotherspeaker u’s voice xuj . The dependency of style and content embedding is minimized with I3. Withcui and su, the decoder reconstructs the voice xui as xui = D(su, cui). Then I2 is calculated basedon the original voice cui and the reconstruction cui. (c) Transfer process: for zero-shot voice styletransfer, with xui from speaker u and xvj from speaker v, we encode content cui and style sv , andcombine them together to generate a transferred voice xu→v,i = D(sv, cui).

Based on the criterion for s in equation (7), a well-learned style encoder Es pulls all style embed-dings sui from speaker u together. Suppose su is representative of the style embeddings of set Xu.If we parameterize the distribution qφ(x|s, c) ∝ exp(−‖x−D(s, c)‖2) with decoder D(s, c), thenbased on Theorem 3.2, we can estimate the lower bound of I(x; c|s) with the following objective:

I2 :=1

N

M∑u=1

Nu∑i=1

[− ‖xui −D(cui, su)‖2 − log

( 1

Nu

Nu∑j=1

exp{−‖xuj −D(cui, su)‖2})].

When maximizing I2, for speaker u with his/her given voice style su, we encourage the contentembedding cui to well reconstruct the original voice xui, with small ‖xui − D(cui, su)‖. Addi-tionally, the distance ‖xuj −D(cui, su)‖ is minimized, ensuring cui does not contain informationto reconstruct other voices xuj from speaker u. With I2, the correlation between xui and cui isamplified, which improves cui in preserving the content information.

3.3 MI UPPER BOUND ESTIMATION

The crucial part of our framework is disentangling the style and the content embedding spaces,which imposes (ideally) that the style embedding s excludes any content information and vice versa.Therefore, the mutual information between s and c is expected to be minimized. To estimate I(s; c),we derive a sample-based MI upper bound in Theorem 3.3 base on (4).

Theorem 3.3. If p(s|c) provides the conditional distribution between variables s and c, then

I(s; c) ≤ E[ 1

N

M∑u=1

Nu∑i=1

[log p(sui|cui)−

1

N

M∑v=1

Nv∑j=1

log p(sui|cvj)]]. (9)

The upper bound in (9) requires the ground-truth conditional distribution p(s|c), whose closed formis unknown. Therefore, we use a probabilistic neural network qθ(s|c) to approximate p(s|c) bymaximizing the log-likelihood F(θ) =

∑Mu=1

∑Nu

i=1 log qθ(sui|cui). With the learned qθ(s|c), the

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objective for minimizing I(s; c) becomes:

I3 :=1

N

M∑u=1

Nu∑i=1

[log qθ(sui|cui)−

1

N

M∑v=1

Nv∑j=1

log qθ(sui|cvj)]. (10)

When weights of encoders Ec, Es are updated, the embedding spaces s, c change, which leadsto the changing of conditional distribution p(s|c). Therefore, the neural approximation qθ(s|c)must be updated again. Consequently, during training, the encoders Ec, Es and the approximationqθ(s|c) are updated iteratively. In the Supplementary Material, we further discuss that with a goodapproximation qθ(s|c), I3 remains an MI upper bound.

3.4 ENCODER-DECODER FRAMEWORK

With the aforementioned MI estimates I1, I2, and I3, the final training loss of our method is

L∗ = [I3 − I1 − I2]− βF(θ), (11)

where β is a positive number re-weighting the two objective terms. Term I3−I1−I2 is minimizedw.r.t the parameters in encoders Ec, Es and decoder D; term F(θ) as the likelihood function ofqθ(s|c) is maximized w.r.t the parameter θ. In practice, the two terms are updated iteratively withgradient descent (by fixing one and updating another). The training and transfer processes of ourmodel are shown in Figure 1. We name this MI-guided learning framework as Information-theoreticDisentangled Embedding for Voice Conversion (IDE-VC).

4 RELATED WORK

Many-to-many Voice Conversion Traditional voice style transfer methods mainly focus on one-to-one and many-to-one conversion tasks, which can only transfer voices into one target speaking style.This constraint limits the applicability of the methods. Recently, several many-to-many voice con-version methods have been proposed, to convert voices in broader application scenarios. StarGAN-VC (Kameoka et al., 2018) uses StarGAN (Choi et al., 2018) to enable many-to-many transfer, inwhich voices are fed into a unique generator conditioned on the target speaker identity. A discrimi-nator is also used to evaluate generation quality and transfer accuracy. Blow (Serra et al., 2019) is aflow-based generative model (Kingma & Dhariwal, 2018), that maps voices from different speakersinto the same latent space via normalizing flow (Rezende & Mohamed, 2015). The conversion isaccomplished by transforming the latent representation back to the observation space with the tar-get speaker’s identifier. Two other many-to-many conversion models, AUTOVC (Qian et al., 2019)and AdaIN-VC (Chou & Lee, 2019), extend applications into zero-shot scenarios, i.e., conversionfrom/to a new speaker (unseen during training), based on only a few utterances. Both AUTOVC andAdaIN-VC construct an encoder-decoder framework, which extracts the style and content of onespeech sample into separate latent embeddings. Then when a new voice from an unseen speakercomes, both its style and content embeddings can be extracted directly. However, as discussed in theIntroduction, both methods do not have explicit regularizers to reduce the correlation between styleand content embeddings, which limits their performance.

Disentangled Representation Learning Disentangled representation learning (DRL) aims to en-code data points into separate independent embedding subspaces, where different subspaces rep-resent different data attributes. DRL methods can be classified into unsupervised and supervisedapproaches. Under unsupervised setups, Burgess et al. (2018), Higgins et al. (2016) and Kim &Mnih (2018) use latent embeddings to reconstruct the original data while keeping each dimensionof the embeddings independent with correlation regularizers. This has been challenged by Locatelloet al. (2019), in that each part of the learned embeddings may not be mapped to a meaningful data at-tribute. In contrast, supervised DRL methods effectively learn meaningful disentangled embeddingparts by adding different supervision to different embedding components. Between the two embed-ding parts, the correlation is still required to be reduced to prevent the revealing of information toeach other. The correlation-reducing methods mainly focus on adversarial training between embed-ding parts (Hjelm et al., 2018; Kim & Mnih, 2018), and mutual information minimization (Chenet al., 2018; Cheng et al., 2020b). By applying operations such as switching and combining, one canuse disentangled representations to improve empirical performance on downstream tasks, e.g. con-ditional generation (Burgess et al., 2018), domain adaptation (Gholami et al., 2020), and few-shotlearning (Higgins et al., 2017).

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5 EXPERIMENTS

We evaluate our IDE-VC on real-world voice a dataset under both many-to-many and zero-shot VSTsetups. The selected dataset is CSTR Voice Cloning Toolkit (VCTK) (Yamagishi et al., 2019), whichincludes 46 hours of audio from 109 speakers. Each speaker reads a different sets of utterances, andthe training voices are provided in a non-parallel manner. The audios are downsampled at 16kHz.In the following, we first describe the evaluation metrics and the implementation details, and thenanalyze our model’s performance relative to other baselines under many-to-many and zero-shot VSTsettings.

5.1 EVALUATION METRICS

Objective Metrics We consider two objective metrics: Speaker verification accuracy (Verification)and the Mel-Cepstral Distance (Distance) (Kubichek, 1993). The speaker verification accuracy mea-sures whether the transferred voice belongs to the target speaker. For fair comparison, we used athird-party pre-trained speaker encoder Resemblyzer1 to classify the speaker identity from the trans-ferred voices. Specifically, style centroids for speakers are learned with ground-truth voice samples.For a transferred voice, we encode it via the pre-trained speaker encoder and find the speaker withthe closest style centroid as the identity prediction. For the Distance, the vanilla Mel-Cepstral Dis-tance (MCD) cannot handle the time alignment issue described in Section 2. To make reasonablecomparisons between the generation and ground truth, we apply the Dynamic Time Warping (DTW)algorithm (Berndt & Clifford, 1994) to automatically align the time-evolving sequences before cal-culating MCD. This DTW-MCD distance measures the similarity of the transferred voice and thereal voice from the target speaker. Since the calculation of DTW-MCD requires parallel data, weselect voices with the same content from the VCTK dataset as testing pairs. Then we transfer onevoice in the pair and calculate DTW-MCD with the other voice as reference.

Subjective Metrics Following Wester et al. (Wester et al., 2016), we use the naturalness of thespeech (Naturalness), and the similarity of the transferred speech to target identity (Similarity) assubjective metrics. For Naturalness, annotators are asked to rate the score from 1-5 for each trans-ferred speech.For the Similarity, the annotators are presented with two audios (the converted speechand the corresponding reference), and are asked to rate the score from 1 to 4. For both scores, thehigher the better. Following the setting in Blow (Serra et al., 2019), we report Similarity definedas a total percentage from the binary rating. The evaluation of both subjective metrics is conductedon Amazon Mechanical Turk (MTurk)2. More details about evaluation metrics are provided in theSupplementary Material.

5.2 IMPLEMENTATION DETAILS

Following AUTOVC (Qian et al., 2019), our model inputs are represented via mel-spectrogram.The number of mel-frequency bins is set as 80. When voices are generated, we adopt the WaveNetvocoder (Oord et al., 2016) pre-trained on the VCTK corpus to invert the spectrogram signal back toa waveform. The spectrogram is first upsampled with deconvolutional layers to match the samplingrate, and then a standard 40-layer WaveNet is applied to generate speech waveforms. Our model isimplememted with Pytorch and takes 1 GPU day on an Nvidia Xp to train.

Encoder Architecture The speaker encoder consists of a 2-layer long short-term memory (LSTM)with cell size of 768, and a fully-connected layer with output dimension 256. The speaker encoderis initialized with weights from a pretrained GE2E (Wan et al., 2018) encoder. The input of thecontent encoder is the concatenation of the mel-spectrogram signal and the corresponding speakerembedding. The content encoder consists of three convolutional layers with 512 channels, and twolayers of a bidirectional LSTM with cell dimension 32. Following the setup in AUTOVC (Qianet al., 2019), the forward and backward outputs of the bi-directional LSTM are downsampled by 16.

Decoder Architecture Following AUTOVC (Qian et al., 2019), the initial decoder consists of athree-layer convolutional neural network (CNN) with 512 channels, three LSTM layers with celldimension 1024, and another convolutional layer to project the output of the LSTM to dimension of80. To enhance the quality of the spectrogram, following AUTOVC (Qian et al., 2019), we use apost-network consisting of five convolutional layers with 512 channels for the first four layers, and

1https://github.com/resemble-ai/Resemblyzer2https://www.mturk.com/

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Table 1: Many-to-many VST evaluation results. For all metrics except Distance, higher is better.

Metric Objective Subjective

Distance Verification[%] Naturalness [1–5] Similarity [%]

StarGAN 6.73 71.1 2.77 51.5AdaIN-VC 6.98 85.5 2.19 50.8AUTOVC 6.73 89.9 3.25 55.0Blow 8.08 - 2.11 10.8IDE-VC (Ours) 6.70 92.2 3.26 68.5

Table 2: Zero-Shot VST evaluation results. For all metrics except Distance, higher is better.

Metric Objective Subjective

Distance Verification[%] Naturalness [1–5] Similarity [%]

AdaIN-VC 6.37 76.7 2.67 68.4AUTOVC 6.68 60.0 2.19 58.6IDE-VC (Ours) 6.31 81.1 3.33 76.4

80 channels for the last layer. The output of the post-network can be viewed as a residual signal.The final conversion signal is computed by directly adding the output of the initial decoder and thepost-network. The reconstruction loss is applied to both the output of the initial decoder and thefinal conversion signal.

Approximation Network Architecture As described in Section 3.3, minimizing the mutual in-formation between style and content embeddings requires an auxiliary variational approximationqθ(s|c). For implementation, we parameterize the variational distribution in the Gaussian distribu-tion family qθ(s|c) = N (µθ(c),σ

2θ(c) · I), where mean µθ(·) and variance σ2

θ(·) are two-layerfully-connected networks with tanh(·) as the activation function. With the Gaussian parameteriza-tion, the likelihoods in objective I3 can be calculated in closed form.

5.3 STYLE TRANSFER PERFORMANCE

For the many-to-many VST task, we randomly select 10% of the sentences for validation and 10% ofthe sentences for testing from the VCTK dataset, following the setting in Blow (Serra et al., 2019).The rest of the data are used for training in a non-parallel scheme. For evaluation, we select voicepairs from the testing set, in which each pair of voices have the same content but come from differentspeakers. In each testing pair, we conduct transfer from one voice to the other voice’s speaking style,and then we compare the transferred voice and the other voice as evaluating the model performance.We test our model with four competitive baselines: Blow (Serra et al., 2019)3, AUTOVC (Qianet al., 2019), AdaIN-VC (Chou & Lee, 2019) and StarGAN-VC (Kameoka et al., 2018). The de-tailed implementation of these four methods are provided in the Supplementary Material. Table 1shows the subjective and objective evaluation for the many-to-many VST task. Both methods withthe encoder-decoder framework, AdaIN-VC and AUTOVC, have competitive results. However, ourIDE-VC outperforms the other baselines on all metrics, demonstrating that the style-content disen-tanglement in the latent space improves the performance of the encoder-decoder framework.

For the zero-shot VST task, we use the same train-validation dataset split as in the many-to-manysetup. The testing data are selected to guarantee that no test speaker has any utterance in the trainingset. We compare our model with the only two baselines, AUTOVC (Qian et al., 2019) and AdaIN-VC (Chou & Lee, 2019), that are able to handle voice transfer for newly-coming unseen speakers.We used the same implementations of AUTOVC and AdaIN-VC as in the many-to-many VST.The evaluation results of zero-shot VST are shown in Table 2, among the two baselines AdaIN-VCperforms better than AUTOVC overall.Our IDE-VC outperforms both baseline methods, on all met-rics. All three tested models have encoder-decoder transfer frameworks, the superior performance

3For Blow model, we use the official implementation available on Github (https://github.com/joansj/blow).We report the best result we can obtain here, under training for 100 epochs (11.75 GPU days on Nvidia V100).

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Figure 2: Left: t-SNE visualization for speaker embeddings. Right: t-SNE visualization for contentembedding. The embeddings are extracted from the voice samples of 10 different speakers.

of IDE-VC indicates the effectiveness of our disentangled representation learning scheme. Moreevaluation details are provided in the supplementary material.

5.4 DISENTANGLEMENT DISCUSSION

Besides the performance comparison with other VST baselines, we demonstrate the capabilityof our information-theoretic disentangled representation learning scheme. First, we conduct a t-SNE (Maaten & Hinton, 2008) visualization of the latent spaces of the IDE-VC model. As shownin the left of Figure 2, style embeddings from the same speaker are well clustered, and style em-beddings from different speakers separate in a clean manner. The clear pattern indicates our styleencoder Es can verify the speakers’ identity from the voice samples. In contrast, the content embed-dings (in the right of Figure 2) are indistinguishable for different speakers, which means our contentencoder Ec successfully eliminates speaker-related information and extracts rich semantic contentfrom the data.

Table 3: Speaker identity pre-diction accuracy on content em-bedding.

Accuracy[%]

AUTOVC 9.5AdaIN-VC 19.0IDE-VC 8.1

We also empirically evaluate the disentanglement, by predictingthe speakers’ identity based on only the content embeddings. Atwo-layer fully-connected network is trained on the testing setwith a content embedding as input, and the corresponding speakeridentity as output. We compare our IDE-VC with AUTOVC andAdaIN-VC, which also output content embeddings. The classi-fication results are shown in Table 3. Our IDE-VC reaches thelowest classification accuracy, indicating that the content embed-dings learned by IDE-VC contains the least speaker-related in-formation. Therefore, our IDE-VC learns disentangled represen-tations with high quality compared with other baselines.

5.5 ABLATION STUDY

Table 4: Ablation study with differenttraining losses. Performance is mea-sured by objective metrics.

Distance Verification[%]

Without I1 9.81 11.1Without I3 6.73 89.4IDE-VC 5.66 92.2

Moreover, we have considered an ablation study that ad-dresses performance effects from different learning lossesin (11), with results shown in Table 4. We compare ourmodel with two models trained by part of the loss func-tion in (11), while keeping the other training setups un-changed, including the model structure. From the results,when the model is trained without the style encoder lossterm I1, a transferred voice still is generated, but with alarge distance to the ground truth. The verification accu-racy also significantly decreases with no speaker-relatedinformation utilized. When the disentangling term I3 is removed, the model still reaches com-petitive performance, because the style encoder Es and decoder D are well trained by I1 and I2.However, when adding term I3, we disentangle the style and content spaces, and improve the trans-fer quality with higher verification accuracy and less distortion. The performance without term I2 isnot reported, because the model cannot even generate fluent speech without the reconstruction loss.

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6 CONCLUSIONS

We have improved the encoder-decoder voice style transfer framework by disentangled latent rep-resentation learning. To effectively induce the style and content information of speech into inde-pendent embedding latent spaces, we minimize a sample-based mutual information upper boundbetween style and content embeddings. The disentanglement of the two embedding spaces ensuresthe voice transfer accuracy without information revealed from each other. We have also derived twonew multi-group mutual information lower bounds, which are maximized during training to enhancethe representativeness of the latent embeddings. On the real-world VCTK dataset, our model out-performs previous works under both many-to-many and zero-shot voice style transfer setups. Ourmodel can be naturally extended to other style transfer tasks modeling time-evolving sequences,e.g., video and music style transfer. Moreover, our general multi-group mutual information lowerbounds have broader potential applications in other representation learning tasks.

ACKNOWLEDGEMENTS

This research was supported in part by the DOE, NSF and ONR.

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