Proceedings of 2022 APSIPA Annual Summit and Conference

7-10 November 2022, Chiang Mai, Thailand

Speak Like a Dog: Human to Non-human creature
Voice Conversion
Kohei Suzuki∗ , Shoki Sakamoto∗ , Tadahiro Taniguchi∗ and Hirokazu Kameoka²∗ Ritsumeikan University, Shiga, Japan

E-mail: {suzuki.kohei, sakamoto.shoki}@em.ci.ritsumei.ac.jp, taniguchi@ci.ritsumei.ac.jp² NTT Corporation, Kanagawa, Japan
E-mail: kameoka.hirokazu@lab.ntt.co.jp

AbstractÐThis paper proposes a new voice conversion (VC)
task from human speech to dog-like speech while preserving
linguistic information as an example of human to non-humancreature voice conversion (H2NH-VC) tasks. Although most VC
studies deal with human to human VC, H2NH-VC aims toconvert human speech into non-human creature-like speech. Nonparallel VC allows us to develop H2NH-VC, because we cannot
collect a parallel dataset that non-human creatures speak humanlanguage. In this study, we propose to use dogs as an example
of a non-human creature target domain and define the ªspeak
like a dogº task. To clarify the possibilities and characteristicsof the ªspeak like a dogº task, we conducted a comparative experiment using existing representative non-parallel VC
methods in acoustic features (Mel-cepstral coefficients and Melspectrograms), network architectures (five different kernel-size
settings), and training criteria (variational autoencoder (VAE)based and generative adversarial network (GAN)-based). Finally,
the converted voices were evaluated using mean opinion scores:
dog-likeness, sound quality and intelligibility, and character error
rate (CER). The experiment showed that the employment of
the Mel-spectrogram improved the dog-likeness of the converted
speech, while it is challenging to preserve linguistic information.
Challenges and limitations of the current VC methods for H2NHVC are highlighted.

I. I NTRODUCTION
Voice conversion (VC) is a technology that converts the
speech waveform of the source speaker into a speech waveformwith the characteristics of the target speaker while preserving
linguistic information [1]. Specifically, in many VC methods,
acoustic features are first extracted from the source speaker’s
speech waveform using speech analysis and then converted
to acoustic features similar to those of the target speaker.
Finally, a speech waveform is synthesized using the converted
acoustic features. Most VC studies have focused on human tohuman VC. In this study, we consider human to non-humancreature VC (H2NH-VC). H2NH-VC converts human voice
into non-human creature-like voice while preserving linguistic
information. Non-human creature-like voice refers to voicewith certain non-human creature elements such as animals andmonsters speaking in a fantasy world. We expect technologies
that can efficiently generate non-human creature-like voice toextend the possibility of creative works in cinema production,
game playing, etc. In this study, we focus on dogs as arepresentative example of the non-human creature target domain because dog voices are relatively easy to collect and arefamiliar and prevalent in our daily lives. We define an H2NHVC task called ªspeak like a dog,º task and investigate theplausibility and challenges of applying existing non-parallel
VC methods in this study. Figure 1 illustrates an overview ofthe human to dog VC.
Notably, the recent development of non-parallel VC methods [2]±[5] has made H2NH-VC a possibility. In studies on
H2NH-VC, the collection of ground truth speech signals has
been a critical barrier to conducting the study. Since ªthere is
no dog in this world that speaks human languages,º the groundtruth utterance output from the VC system corresponding to
the input utterance cannot be obtained. Therefore, conventional
VC methods that require parallel data for training, parallel
VC methods [6]±[8], cannot be used. For more examples anddetails of parallel VC methods, readers are referred to a recent
review article [9].
The parallel data consists of source and target speech pairs
used to express the same sentence. In contrast, non-parallel
VC methods do not require parallel data. This means that nonparallel VC methods have theoretically overcome the barrierof H2NH-VC. Recent deep learning-based, non-parallel VC
methods have achieved a VC that is comparable to the groundtruth speech in a mean opinion score (MOS) test [10], [11].
The baseline methods in Voice Conversion Challenge [12] arebased on generative adversarial networks (GAN) or variational
autoencoders (VAE). One example of a GAN-based method is
StarGAN-VC [13], [14], and one example of a VAE-based
method is the auxiliary classifier VAE-based VC (ACVAEVC) [15]. However, the applicability of these non-parallel VC
methods to H2NH-VC tasks has not been explored.
To this end, in this paper, we propose a task called ªspeak
like a dog,º which is a new task for VC from human voice
into dog-like voice while preserving linguistic information.
We construct datasets and evaluation criteria for this task. In
addition, we investigated how much VC could be achieved
using existing non-parallel VC methods. We experimented
with comparing acoustic features, network architectures, andtraining criteria as baseline methods.
The main contributions of this paper are twofold.
• We propose the ªspeak like a dogº task as an example of
H2NH-VC tasks and construct a dataset and evaluation
criteria.
• We investigated the possibilities and characteristics of

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Proceedings of 2022 APSIPA Annual Summit and ConferenceDog voice

Human speech

Ikita junkatsuyu desu.

Dog dataset. The dog dataset was constructed from several
studies [17]±[19], Freesound project [20], freesoundslibrary,
and Youtube. Because there is no dog dataset created for VC,
we removed extremely soft, loud, and noisy sounds from ourcollected data. We also divided them into two datasets basedon pitch, adult dog and puppy. The number of adult dog andpuppy sounds is 792 and 288, respectively. The total time for
adult dogs and puppies is 36 and 29 minutes, respectively.
Note that the total time includes much of the time between
barks. The dataset is available under the terms of the Creative
Commons Attribution-NonCommercial license.

"Bow wow""Bow wow, bow wow"
"Woof, woof""Isshuukan bakari nyuyo-ku wo shuzai shita.""Ryokan ya hoteru ni
tuku to hijouguchi wotazuneru."

Optimized
parameter

7-10 November 2022, Chiang Mai, Thailand

Ikita junkatsuyu desu.

C. Evaluation criteria
Fig. 1. Overview of the human to dog VC. The linguistic information in the
training data may differ between speakers.

ªspeak like a dogº task by conducting an experiment comparing existing representative non-parallel VC methods
in acoustic features, network architectures, and training
criteria.
The remainder of this paper is organized as follows. Section
2 defines the speech task as a dog task. Section 3 describes
the methods used in the experiment. Section 4 describes
comparative experiments. Finally, Section 5 concludes thepaper.
II. S PEAK L IKE A D OG TASK
A. Problem definition
The proposed ªspeak like a dogº task is one of the H2NHVC tasks that converts human voice into dog-like voice while
preserving linguistic information and representing a dog-likeelement of the target domain. Thus, the VC method should preserve linguistic information and represent dog-like elements. It
is not enough to satisfy only one or the other. In particular, it
is important to preserve linguistic information, that is, utteredsentences are recognized correctly by listeners, while ensuring
dog-likeness, sound quality, and intelligibility of speech.

Mean Opinion Score (MOS). We defined three MOSs forthe ªspeak like a dogº task. They can be obtained by asking
subjects to rate the following three MOS tests on a scale of 1
to 5:1) Dog-likeness: How much of the dog-like element isincluded?
(1 indicates completely not dog-like, and 5 indicatescompletely dog-like).
2) Sound quality: How good is the sound quality?
(1 indicates completely low quality, and 5 indicatescompletely high quality.)3) Clarity: How intelligibly were you able to hear the
spoken utterance given a written text of the content of
the spoken utterance?
(1 indicates complete vagueness, and 5 indicates complete intelligibility).
Character Error Rate (CER). To evaluate how H2NHVC preserves linguistic information, we use the character
error rate (CER), which represents the error rate between thetranscribed sentences of converted speech by the listener andthe correct sentences. The CER is defined as
D+S+I
,
(1)
CER =
N
where D, S, I, and N are the number of deletion errors, substitution errors, insertion errors, and characters in the reference,
respectively.

B. Dataset

III. M ETHOD

A dataset can be non-parallel but should contain humanspeech signals and dog voices. We constructed an example
dataset for the ªspeak like a dogº task. Details of the dataset
can be found on our website1 . The abstract of the dataset is
as follows.
ATR digital sound database. We use the ATR digital sounddatabase [16] for human speech signals. This is a databaseof speech recordings of sentences, single words, and otherstandardized content uttered by two male (MMY and MTK)
and two female (FKN and FTK) professional announcers. The
number of each domain’s sounds is 503. Total time for FKN
and MMY dataset is 45 and 39 minutes, respectively.
1 https://github.com/suzuki256/dog-dataset

In this section, we introduce the methods for the experiment.
The key elements involved in VC are acoustic features, network architectures, and the methods of learning the conversion
model. Each element has multiple options, but it is not clear
what combination is appropriate for converting human voice
to dog-like voice.
A. Acoustic features
The main acoustic features transformed by VC are MCC
sequences and the mel-spectrogram. MCC is an acoustic
feature that corresponds to the spectral envelope (shape of thehuman vocal tract), and the mel-spectrogram is an acoustic
feature that includes a harmonic structure (close to the raw
acoustic spectrum). Therefore, it is not clear whether MCC

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𝒄𝑐𝒓𝝍 (𝒄|𝒙)𝐩𝒛𝒑𝜽 (𝐱|𝒛, 𝑐)

𝝈𝟐 𝜽

𝝁𝝋

𝝈𝟐 𝝋

𝒒𝝋 (𝐳|෤𝐱, 𝑐)ǁ

𝐱

7-10 November 2022, Chiang Mai, Thailand

𝑐ǁ

𝐱෤

𝝁𝜽

Proceedings of 2022 APSIPA Annual Summit and Conference𝒄′Fig. 2. Overview of ACVAE-VC training [15]. x̃ and x denote acoustic feature
sequences. c̃ and c denote speaker information. z denote latent variable.
qφ (z|x̃, c̃) output the mean µφ and variance σφ2 of the latent variable z following
a normal distribution. pθ (x|z, c) output the mean µθ and variance σθ2 of the xthat follows a normal distribution. rψ (c|x) output distribution p which speakeruttered the acoustic feature sequences x.

is appropriate for representing a dog’s spectral envelope. We
predict that the mel-spectrogram works better than MCC in
the ªspeak like a dogº task. We then compared the results ofthe experiments.
B. Network architecture
Network architectures is another element to investigate.
In recent years, both convolutional neural network (CNN)based [21]±[24] and recurrent neural network (RNN)-based
network architectures [25]±[27] have been employed in VC
studies. For example, StarGAN-VC is based on three networks,
a generator, discriminator, and domain classifier, with CNN
architectures.
This study focuses on a CNN-based network architecturethat can be easily investigated by changing the kernel size. We
want to investigate how much time range the VC system needs
to capture in the ªspeak like a dogº task. In the experiment, we
focus on the kernel size k because it is not obvious how widea range of time dependencies must be captured in acquiring amodel for converting human voice to dog-like voice.
Notably, unlike humans, dogs’ voice is short. Therefore,
reducing the kernel size of the discriminator and the domainclassifier reduces the receptive field of the CNN. That may
potentially results in more dog-like voice. However, increasing
the kernel size increases the amount of information the CNN
receives and may allow for a conversion that comes closer tohuman voice, preserving linguistic information.
C. Training criterion
A VAE-based VC method and a GAN-based VC method
have been proposed to enable non-parallel VC. In this study,
we used StarGAN-VC and ACVAE-VC because they are
known to function in standard VC tasks as benchmark methods
and a type of major generative model.
ACVAE-VC. Figure 2 illustrates an overview of ACVAE-VC.
ACVAE-VC is a VC method that applies the regularization
concept of InfoGAN [28] to conditional VAE (CVAE) [29] Oneof the strengths of this method is its fast learning convergenceand stable acquisition of high-performance conversion models.

𝐱
𝐱ො

𝒄

𝐺

𝒄

𝐷

𝒚
𝐺(𝐱, 𝑐)

𝐺

𝒄′

𝐶

𝒄ො

Fig. 3. Overview of StarGAN-VC training [13], [14].

to-many conversion models on non-parallel data based on
StarGAN’s [30] learning method. One of the strengths of thismethod is the possibility of obtaining a conversion model that
performs as well as or better than ACVAE-VC, if hyperparameters that allow for good learning convergence can be found.
IV. E XPERIMENT
A. Conditions
The speaker information used for training the VC models were (ªFKN,º ªMMY,º ªpeople (FKN, FTK, MMY, andMTK),º ªadult dog,º ªpuppy,º and ªdogs (adult dog andpuppy)º). Ten recording samples were randomly selected fromeach domain as evaluation data. The remaining data of eachdomain were used as the training data. The sounds usedin the evaluation is that converted from FKN to adult dog,
original sounds of FKN and adult dog before the conversion,
and white noise. Five listeners took part in experiment. The
listeners participated in each MOS test only once. Regarding
CER tests, each converted voice was presented to a listener
twice to prevent the listener from missing to listen to it.
In this experiment, we generated a speech waveform using
WORLD [31] (D4C edition [32]) and parallel WaveGAN [33],and the acoustic features input to the VC model were the
MCC sequences and mel-spectrogram, respectively. We trained
Parallel WaveGAN with the same dataset as the VC method.
Experiment 1: comparing acoustic features and learning
methods. We performed VC using StarGAN-VC with MCC
and the mel-spectrogram and ACVAE-VC with MCC and themel-spectrogram using default kernel sizes in original paper.
Experiment 2: comparing the kernel size k of the conversion model (CNN). We define the kernel size kd of thediscriminator and domain classifier described in the original
paper as default values. The kernel size kd − 2, kd − 1, kd ,kd +1, and kd +2 in the time direction were compared. Figure 4
illustrate the original paper’s architectures for the discriminatorand domain classifier, respectively. Bold letters indicate thedefault kernel size kd for each layer. The value kd was changedin this experiment.
B. Result

StarGAN-VC. Figure 3 illustrates an overview of StarGANVC. StarGAN-VC is a VC method that can train many-

Tables I and II show the results of the MOS and CER
tests in Experiment 1. In all Tables, ªmelspecº and ªoriginalº

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Proceedings of 2022 APSIPA Annual Summit and Conference

7-10 November 2022, Chiang Mai, Thailand

Real/Fake Discriminator

TABLE IIIR ESULT OF THE MOS TEST IN EXPERIMENT 2

Attribute c

Kernel size
kd +2
kd +1
kd
kd -1
kd -2
FKN (original)
Adult Dog (original)
White Noise

Domain Classifier

Dog-likeness
2.20
2.40
2.60
2.28
2.60
1.00
5.00
1.10

Sound
quality
2.40
2.08
2.80
2.28
2.48
4.70
3.20
1.00

Clarity
2.00
2.12
2.60
3.00
2.88
5.00
1.00
1.00

TABLE IVR ESULT OF THE CER IN E XPERIMENT 2

Fig. 4. Network architecture of the discriminator and classifier [14], [30]. In
input and output, ªhº, ªwº, and ªcº represent height, width, and number ofchannels, respectively. In addition, ªkº, ªcº, and ªsº denote kernel size, numberof channels, and stride size, respectively. Conv1d, BatchNorm, GLU, andDeconv1d denote the 1D convolution layers, batch normalization, gated linear
units, and 1D transposed convolution layers, respectively. Batch normalization
is applied to each input channel. The class index vectors were repeated along
the time direction and then concatenated to the input of each convolution layer.
TABLE IR ESULT OF THE MOS TEST IN E XPERIMENT 1
Methods
StarGAN-VC (MCC)
StarGAN-VC (melspec)
ACVAE-VC (MCC)
ACVAE-VC (melspec)
FKN (original)
Adult Dog (original)
White Noise

Dog-likeness
1.20
4.20
2.04
4.24
1.00
5.00
1.10

Sound
quality
1.28
2.76
2.24
2.36
4.80
3.70
1.20

Clarity
0.92
2.04
1.76
1.36
5.00
1.00
1.00

denote the mel-spectrogram and the unconverted test data,
respectively. The bold and underlined numbers in all the tables
also denote the highest numerical value of conversion humanvoice to dog-like voice and the value of voice expected toobtain the highest numerical value in each MOS evaluation,
respectively. The notation inside parentheses next to each VC
method in Table I denotes the input acoustic features.
Table I shows that the method using the mel-spectrogram
produces a more dog-like voice than the method using the
MCC. Regarding sound quality, StarGAN-VC with a melspectrogram produced the best results. It is clear that StarGANVC with a mel-spectrogram also has the highest clarity of
TABLE IIR ESULT OF THE CER TEST IN E XPERIMENT 1
Methods
StarGAN-VC (MCC)
StarGAN-VC (melspec)
ACVAE-VC (MCC)
ACVAE-VC (melspec)
FKN (original)

1st sound
1.00
0.97
0.97
0.98
0.03

2nd sound
1.00
0.95
0.94
0.97
0.02

Kernel size
kd +2
kd +1
kd
kd -1
kd -2
FKN (original)

1st sound
0.93
0.95
0.97
0.83
0.87
0.03

2nd sound
0.89
0.92
0.95
0.80
0.76
0.02

spoken utterances. In contrast, for ACVAE-VC, we observedthat the use of MCC as an acoustic feature synthesizes clearer
voice than the use of a mel-spectrogram.
Table II indicates that there is no significant improvementin CER while the ACVAE-VC with MCC has the smallest
error. This shows that the non-parallel VC methods did not
successfully achieve the ªspeak like a dogº task preserving
linguistic information satisfactorily. However, we can noticesome language-like sentences were made from converted voice
when listening to the sound generated by ACVAE-VC (MCC).
That did not contribute to CER much.
Table III and IV show the results of the MOS and CER test
in Experiment 2. From Table III, it is clear that the kernel sizeof the default value and the kernel size of the default value
minus 2 produce a relatively better dog-like voice. Regarding
sound quality, we find that the default values are the best. It
can be seen that the clarity of the spoken utterance is best at thekernel size of the default value minus 1. Table IV indicates thatthe kernel size of the default value −1or −2 has the smallest
error.
C. Discussion
In the results of the MOS test in Experiment 1, the melspectrogram may have a better representation of dog-like voicethan the MCC, as predicted by Section III-A.
There was no significant difference for dog-like elements between StarGAN-VC and ACVAE-VC using the melspectrogram in quantitative evaluation. However, when we listened to the converted sounds, we found qualitative differences
subjectively. Sounds produced by ACVAE-VC had a tendency
to preserve language-like expressions. The converted sounds
can be listened to on our website1 .
From the MOS values for clarity in Table I and the CERvalues in Table II, ACVAE-VC with the mel-spectrogram

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Proceedings of 2022 APSIPA Annual Summit and Conference

7-10 November 2022, Chiang Mai, Thailand

preserves less clarity and linguistic information than ACVAEVC with the MCC. This may be because the source filter
model on which the vocoder is based imitates the human voice
production process.
In the results of the CER test of Experiment 1, the highvalue of CER with existing methods indicates the difficultyof preserving linguistic information in the ªspeak like a dogºtask.
In Experiment 2, unlike what was predicted in Section IV-A,
increasing the kernel size from the default value did not
improve the preservation of linguistic information. This may
be because the discriminator and the domain classifier weretrained to include the dog’s voice and silent intervals by
increasing the kernel size. We found that relatively small kernel
size, e.g., kd − 1 or kd − 2, had a better performance in theªspeak like a dogº task, generally.

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V. C ONCLUSIONSIn this study, we proposed the ªspeak like a dogº task as an
example of a human to non-human creature VC task. Although
we could convert human voices into dog-like voices in a
fragmented manner, we found that it is challenging to preserve
linguistic information. It was clear that VC methods thatworked well for standard VC tasks did not work sufficiently
well here. We also found that using a mel-spectrogram insteadof an MCC is important in this task.
VI. ACKNOWLEDGEMENTS
This study was supported by the Japan Society for the
Promotion of Science (JSPS) KAKENHI Grant-in-Aid for
Scientific Research (A), grant number 21H04904, and JST
CREST Grant JPMJCR19A3.
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