TRAINING ALGORITHM TO DECEIVE ANTI-SPOOFING VERIFICATIONFOR DNN-BASED SPEECH SYNTHESIS
Yuki Saito, Shinnosuke Takamichi, and Hiroshi Saruwatari
Graduate School of Information Science and Technology, The University of Tokyo,
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Email: {yuuki saito, shinnosuke takamichi, hiroshi saruwatari}@ipc.i.u-tokyo.ac.jp
ABSTRACTThis paper proposes a novel training algorithm for high-quality Deep
Neural Network (DNN)-based speech synthesis. The parameters ofsynthetic speech tend to be over-smoothed, and this causes significant quality degradation in synthetic speech. The proposed algorithm takes into account an Anti-Spoofing Verification (ASV) as anadditional constraint in the acoustic model training. The ASV isa discriminator trained to distinguish natural and synthetic speech.
Since acoustic models for speech synthesis are trained so that the
ASV recognizes the synthetic speech parameters as natural speech,
the synthetic speech parameters are distributed in the same manner
as natural speech parameters. Additionally, we find that the algorithm compensates not only the parameter distributions, but also theglobal variance and the correlations of synthetic speech parameters.
The experimental results demonstrate that 1) the algorithm outperforms the conventional training algorithm in terms of speech quality,and 2) it is robust against the hyper-parameter settings.
Index Terms— DNN-based speech synthesis, anti-spoofing verification, training algorithm, generative adversarial training, multitask learning
1. INTRODUCTION
Statistical parametric speech synthesis [1] is a technique that aims togenerate natural-sounding synthetic speech. Hidden Markov Models (HMMs) [2] and Deep Neural Networks (DNNs) [3] are used as
the acoustic models for speech synthesis, and these acoustic models are trained with several training algorithms such as the maximum likelihood criterion [2] and Minimum Generation Error (MGE)criterion [4, 5]. Acoustic modeling techniques for generating highquality speech are widely studied since they can be used for text-tospeech, voice conversion, and multimodal speech synthesis. However, the speech parameters generated from these models tend to beover-smoothed, and the quality of their speech is still low comparedwith that of natural speech [1, 6].
One promising way to improve speech quality is to compensate
for the divergence between the natural and generated speech parameters. The parameter distributions of natural and synthetic speech
are significantly different [7], but quality improvements can be hadby transforming the synthetic speech parameters so that their distribution is close to that of natural speech parameters. This can bedone by, for example, modeling the probability distributions in aparametric [8] or non-parametric [9] way in the training stage, andthen, generating or transforming the synthetic speech parameters byusing the distributions. The more effective approach is to use analytically derived features correlated to the quality degradation of thesynthetic speech. Global Variance (GV) [8] and Modulation Spectrum (MS) [10] are well-known examples. They work as an addi-

tional term in the training/synthesis stage [11, 12]. Nose and Ito [13]and Takamichi et al. [11] proposed methods that reduce the difference between Gaussian distributions of natural and generated GV
and MS. However, quality degradation is still a critical problem.
In order to address this quality problem, this paper proposesa novel training algorithm that uses an Anti-Spoofing Verification(ASV) for statistical parametric speech synthesis. The ASV is a discriminator trained to distinguish natural and synthetic speech. Since
the acoustic models for speech synthesis are trained to deceive theASV, the parameter distribution of the synthetic speech is close tothat of natural speech. The training criterion is the weighted sum of
the conventional training criterion and the spoofing rate for the ASV,
and the training is simply done using a backpropagation algorithm byusing DNNs for speech synthesis and ASV. Furthermore, the training
algorithm can be regarded as a generalization of the conventional approaches; i.e., 1) we use not only analytically derived features (e.g.,
GV and MS), but also automatically derived (DNN-derived) features
as the compensated features, and 2) we model a probability distribution that is more complicated than the conventional Gaussian distribution. We conducted an experimental evaluation to demonstrate theeffectiveness of our algorithm. The experimental results demonstratethat 1) the algorithm outperforms the conventional MGE training interms of speech quality and 2) it works comparably robustly against
the hyper-parameter settings.
2. CONVENTIONAL TRAINING ALGORITHM
2.1. DNNs as acoustic models
In DNN-based speech synthesis [14], acoustic models that represent a relationship between linguistic features and speech parameters consist of layered hierarchical networks. To construct
the models, we minimize a loss function calculated using natural and synthetic speech parameters. Let c be a natural speech
[
]
⊤
⊤ ⊤
parameter sequence c⊤, and ĉ be a syn1 , · · · , ct , · · · , cT
[ ⊤
]
⊤ ⊤
thetic speech parameter sequence ĉ1 , · · · , ĉ⊤, wheret , · · · , ĉTt and T denote the frame index and total frame length, respectively. ct = [ct (1) , · · · , ct (D)]⊤ is a D-dimensional speechparameter vector at frame t. As in [14], the acoustic models described here predict static and dynamic speech feature vectors,
[
]
⊤
⊤ ⊤
ôt = ĉ⊤, at frame t.
t , ∆ĉt , ∆∆ĉt2.2. Minimum Generation Error (MGE) training [5]The acoustic models are trained using the Minimum Generation Error (MGE) training algorithm [5]. The loss function LG (c, ĉ) is defined as the mean squared error between natural and synthetic speech

Generated
speechparams.

Linguisticfeats.

DNN-based
acoustic models &parameter generation

Naturalspeechparams.

or
0: generated
1:
naturalDNN-basedanti-spoofing verification

FeaturefunctionFig. 1. Calculation of loss function in proposed algorithm.
parameters as follows:1(ĉ − c)⊤ (ĉ − c)T1
= (Rô − c)⊤ (Rô − c) ,
(1)
T
⊤ ⊤
⊤
⊤
where ô = [ô1 , · · · , ôt , · · · , ôT ] is the static and dynamic
speech feature sequence predicted by the acoustic models. R is a
DT -by-3DT matrix given as()−1
R = W ⊤ Σ−1 WW ⊤ Σ−1 ,(2)LG (c, ĉ) =where W is a 3DT -by-DT matrix for calculating dynamic features [2] and Σ = diag [Σ1 , · · · , Σt , · · · , ΣT ] is a 3DT -by-3DT
covariance matrix, where Σt is a 3D-by-3D covariance matrix at
frame t. Σ is separately estimated using training data.
The model parameters (e.g., weight or bias of DNNs) are estimated using the backpropagation algorithm using the gradients of
L (c, ĉ) by o.
3. PROPOSED TRAINING ALGORITHM
3.1. Anti-Spoofing Verification (ASV) [15]In order to distinguish natural speech and synthetic speech, an ASV
discriminator is trained using the speech parameters (or speech
waveforms). In DNN-based ASV (e.g., [16]), after applying the feature function ϕ (·) to the input speech parameters, the DNN outputs
the posterior probability D (ϕ (·)) that the input is natural speech.
Features that distinguish natural and synthetic speech parameters areoften calculated at this point. Here, frame-wise speech parameters
are directly used for the ASV, i.e., ϕ (ct ) = ct . The loss function
for the ASV is defined as the cross-entropy function:
LD (c, ĉ) = LD,1 (c) + LD,0 (ĉ) ,LD,1 (c) = −

T
1 ∑

(3)

log D (ct ) ,

(4)

1 ∑
log (1 − D (ĉt )) .
T t=1

(5)

T t=1
T

LD,0 (ĉ) = −where LD,1 (c) and LD,0 (ĉ) are the loss functions for natural andsynthetic speech parameters, respectively. The backpropagation algorithm is used to train the DNN to output 1 for natural speech and
0 for synthetic speech.
3.2. Training algorithm to deceive ASV
Here, we describe a novel training algorithm to deceive the ASV.
Fig. 1 illustrates the computation procedure of the loss function.

The loss function for speech synthesis is
ELG
L (c, ĉ) = LG (c, ĉ) + ωD
LD,1 (ĉ) ,(6)ELDwhere ELG and ELD denote the expectation values of LG (c, ĉ)and LD,1 (ĉ), respectively. Their ratio, ELG /ELD is the scale normalization term between LG (c, ĉ) and LD,1 (ĉ), and the hyperparameter ωD controls the weight of the second term. When ωD =0, the loss function is equivalent to the conventional MGE training, and when ωD = 1, LG (c, ĉ) and LD,1 (ĉ) have equal weights.
LD,1 (ĉ) lets the synthetic speech parameters be recognized as natural speech in the ASV step, and it minimizes the Jensen-Shannon
divergence between the distributions of the natural and synthetic
speech parameters [17]. Therefore, this loss function not only minimizes the generation error but also makes the distribution of thesynthetic speech parameters close to that of natural speech parameters.
After training the acoustic models to deceive the ASV, the ASV
is retrained to distinguish natural and synthetic speech. The final
acoustic models are estimated by iterating these algorithms.
3.3. Relation to other methods and behavior analysis
We can choose not only analytically derived features, e.g., GV andMS, but also automatically derived features (e.g., [18]) for the ASV
in our training algorithm. When an analytically derived feature isused, it must be differentiable by the input speech parameters for the
backpropagation algorithm to work. Also, since only the backpropagation algorithm is used for training, a variety of DNN architectures(e.g., Long Short-Term Memory (LSTM) [19]) can be used as acoustic models and the ASV.
The proposed loss function (Eq. (6)) is a combination of a multitask learning algorithm using discriminators [20] and a generative
adversarial training [17]. In defining L (c, ĉ) = LD,1 (ĉ), the loss
function is equivalent to that for generative adversarial training [17].
In other words, our algorithm can be regarded as generative adversarial training with referred input and output parameters [21].
As described above, our algorithm makes the distribution of thesynthetic speech parameters close to that of natural speech parameters. Since we perform generative adversarial training with DNNs,
our algorithm comes to have a more complicated probability distribution than the conventional Gaussian distribution. Fig. 2 plots natural and synthetic speech parameters with several mel-cepstral coefficient pairs. Whereas the parameters of the conventional algorithm
are narrowly distributed, those of the proposed algorithm are widelydistributed, the same as the natural speech parameters. Moreover,
we can see that the proposed algorithm more affects the distributionof the higher order of the mel-cepstral coefficients.
Here, one can explore what components (e.g., analytically derived features and intuitive reasons [22]) the algorithm changes.
Fig. 3 plots the averaged GVs of natural and synthetic speech parameters. We can see that the GV created by the proposed algorithm
is closer to the natural GV than is the one produced by the conventional algorithm. Then, we calculated a Maximal Information
Coefficient (MIC) [23] to quantify a correlation between each dimension of the mel-cepstral coefficients. Fig. 4 shows the results.
As reported in [7], we can see that there are weak correlations between dimensions of the natural speech parameters, whereas strong
correlations are observed between those of generated speech parameters of the MGE training. Meanwhile, the generated mel-cepstralcoefficients of our algorithm have weak correlations than those of
the MGE training. These results suggest that the proposed algorithm

MGE

23rd mel-cepstralcoefficient

13th mel-cepstralcoefficient

Global variance3rd mel-cepstralcoefficient

Natural101

Proposed(ωD =1.0)

1st mel-cepstral coefficient

NaturalProposed (ωD =1.0)
MGE

100
10-1
10-2
10-3
10-4

0

5

10
15
Feature index

20

Fig. 3. Averaged GVs of synthetic mel-cepstral coefficients.

11th mel-cepstral coefficient

Natural24

Proposed(ωD =1.0)

MGE

1.0
0.8

18

0.6

12

0.4

6

21st mel-cepstral coefficientFig. 2. Scatter plots of mel-cepstral coefficients with several pairsof dimensions. From the left, the figures correspond to natural
speech, the conventional MGE algorithm, and the proposed algorithm (ωD = 1.0), respectively. These mel-cepstral coefficientswere extracted from one utterance of the evaluation data.
compensates not only the GV of the synthetic speech parameter, butalso the correlation between each dimension of the parameters.
4. EXPERIMENTAL EVALUATION
4.1. Experimental conditions
We used speech data of a male speaker taken from the ATR Japanese
speech database [24]. The speaker uttered 503 phonetically balanced
sentences. We used 450 sentences (subsets A to I) for the training
and 53 sentences (subset J) for the evaluation. Speech signals were
sampled at a rate of 16 kHz, and the shift length was set to 5 ms. The
0th-through-24th mel-cepstral coefficients were used as a spectral
parameter and F0 and 5 band-aperiodicity [25, 26] were used as excitation parameters. The STRAIGHT analysis-synthesis system [27]was used for the parameter extraction and the waveform synthesis.
To improve training accuracy, speech parameter trajectory smoothing [28] with a 50 Hz cutoff modulation frequency was applied to thespectral parameters in the training data. Linguistic features of contexts consisted of 274-dimensional vector including phonemes, mora
position, accent type, frame position in a phoneme, and so on. We
only used some of the prosody-related features because we predicted
only the spectral parameters in this experiment. In the training phase,
spectral features were normalized to have zero-mean unit-variance,and 80% of the silence frames were removed from the training datain order to reduce the computational cost.
The DNN architectures for acoustic models and ASV were
Feed-Forward networks. The architecture for the acoustic models
included 3 × 400-unit Rectified Linear Unit (ReLU) [29] hidden

0

0.2
0

6

12

18

24 0

6

12

18

24 0

6

12

18

24

0.0

Fig. 4. MICs of natural and synthetic mel-cepstral coefficients. The
MIC ranges from 0.0 to 1.0, and the two valuables with a strong
correlation have a value closer to 1.0. From the left, the figures correspond to natural speech, the conventional MGE algorithm, and the
proposed algorithm (ωD = 1.0), respectively. These MICs were
calculated from one utterance of the evaluation data.
layers and a 75-unit linear output layer. The architecture for the
ASV included 2 × 200-unit ReLU hidden layers and a one-unit sigmoid output layer. The acoustic models output static and dynamic
mel-cepstral coefficients (75-dim.) frame by frame. The ASV input
static mel-cepstral coefficients (25-dim.) frame by frame. We used
AdaGrad [30] as the optimization algorithm, setting the learning rate
to 0.01. F0 , band-aperiodicity, and duration of natural speech wereused for the speech waveform synthesis.
In the training phase, we performed the training algorithm [14]frame-by-frame for the initialization of acoustic models; then we
performed the conventional MGE training [5] with 25 iterations.
Here, “iteration” means using all the training data (450 utterances)
once for training.
The ASV was initialized using natural speech and synthetic
speech after the MGE training. The number of iterations for the
ASV initialization was 5. The proposed training and ASV re-training
were performed with 25 iterations. At each iteration step, the proposed training algorithm updated the acoustic model parameters byusing all the training data. The ASV re-training was performed using speech parameters generated with the updated acoustic models.
Expectation values ELG and ELD were estimated at each iteration
step.
In order to evaluate our algorithm, we calculated the parameter generation loss defined in Eq. (1) and spoofing rate of the synthetic speech. The spoofing rate is the number of spoofing synthetic
speech parameters divided by the total number of synthetic speechparameters in the evaluation data. Here, “spoofing synthetic speechparameter” indicates a parameter for which the ASV recognized thesynthetic speech as natural speech. The ASV for calculating thePreference score

0.75
Proposed

Generation loss

0.70

(ωD =1.0)

Proposed

0.65(ωD =0.3)

0.60

MGE

0.55

0.0

0.50

Spoofing rate

0.995

0.4

0.990

0.00.0

0.6

0.8

1.0

between “Proposed (ωD = 0.3)” and “Proposed (ωD = 1.0).”
Since the objective results shown in Fig. 5 do not vary much when
ωD is between 0.3 and 1.0, it is expected that the speech quality
will be almost the same between 0.3 and 1.0. Therefore, this result
suggests that our algorithm works comparably robustly against thehyper-parameter setting.

1.000

0.6

0.2

0.4

Fig. 6. Preference scores of speech quality with 95% confidence
intervals.

0.45
1.0
0.8

0.2

0.985
0.980

0.2

5. CONCLUSION

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.4

0.6

0.8

1.0

ωD
Fig. 5. Parameter generation loss (above) and spoofing rate (below)
for various ωD .
spoofing rates was constructed using natural speech parameters andsynthetic speech parameters of the conventional MGE training. The
generation loss and spoofing rates were first calculated with various
hyper-parameter ωD settings. Then, we conducted a subjective evaluation of the quality of the synthetic speech.
4.2. Objective evaluation
Fig. 5 shows the results for the generation loss and spoofing rate. As
ωD increases from 0.0, the generation loss monotonically increases,
but from 0.4, we cannot see any tendency. On the other hand, thespoofing rate significantly increases as ωD increases from 0.0 to 0.2;
from 0.2, the value does not so vary. These results demonstrate thatthe proposed training algorithm makes the generation loss worse but
can train the acoustic models to deceive the ASV.
4.3. Subjective evaluation
A preference test (AB test) was conducted to evaluate the quality ofspeech produced by the algorithm. We generated speech samples
with three methods:
MGE: conventional MGE (= proposed (ωD = 0.0))
Proposed (ωD = 0.3): spoofing rate > 0.99
Proposed (ωD = 1.0): standard setting of ωD
We presented every pair of synthetic speech of the three sets in
random order and forced listeners to select the speech sample thatsounded better in quality. Eight listeners participated in the assessment.
Fig. 6 shows the results. The resulting scores of “Proposed”
were much higher than those of “MGE.” Thus, our algorithm yieldeda remarkable improvement in terms of speech quality. Moreover,
we can see from the results that there is no significant differenceThis paper proposed a novel training algorithm for high-quality Deep
Neural Network (DNN)-based speech synthesis. An Anti-Spoofing
Verification (ASV), which distinguishes natural speech and synthetic
speech, is used to train the acoustic models of speech synthesis. The
acoustic models are trained to not only minimize the generation loss
but also make the parameter distribution of the synthetic speech parameters close to that of natural speech parameters. We found that
our algorithm compensated not only global variance but also correlation of synthetic speech parameters. The experimental results
showed significant improvements in terms of speech quality. Moreover, it was demonstrated that the algorithm works comparably robustly against the hyper-parameter settings. In the future, we will
use the features that are effective for the ASV, further investigate thebehavior in relation to the hyper-parameter settings, and devise ASV
models with temporal [31] and linguistic [21] dependencies.
Acknowledgements: Part of this work was supported by ImPACT Program of Council for Science, Technology and Innovation(Cabinet Office, Government of Japan), SECOM Science and Technology Foundation, and JSPS KAKENHI Grant Number 16H06681.
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