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Title

Increasing Speech Intelligibility by Mimicking
Professional Announcers’ Voices and Its PhysicalCorrelates

Author(s)Tran, Dung Kim; Akagi, Masato; Unoki, Masashi

Citation

2023 Asia Pacific Signal and Information
Processing Association Annual Summit andConference (APSIPA ASC): 1187-1192

Issue Date

2023-10-31

Type

Conference Paper

Text version

author

URL

http://hdl.handle.net/10119/18769

RightsThis is the author's version of the work. Copyright(C) 2023 IEEE. 2023 Asia Pacific Signal andInformation Processing Association Annual Summit
and Conference (APSIPA ASC), Taipei, Taiwan,
2023, pp. 1187-1192, doi:
10.1109/APSIPAASC58517.2023.10317261.
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Description

2023 Asia Pacific Signal and Information
Processing Association Annual Summit andConference (APSIPA ASC), October 31 - November
3, 2023, Taipei, Taiwan

Japan Advanced Institute of Science and Technology

2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

Increasing Speech Intelligibility by
Mimicking Professional Announcers’ Voices and
Its Physical Correlates
Dung Kim Tran∗ , Masato Akagi∗ , and Masashi Unoki∗∗ School of Information Science, Japan Advanced Institute of Science and Technology,

1-1 Asahidai, Nomi, Ishikawa, 923-1292 Japan
E-mail: {kimdungtran,akagi,unoki}@jaist.ac.jp

Abstract—Previous studies found that speech uttered by professional announcers is more intelligible than that by nonexperts in noisy environments. On the basis of this finding, wedeveloped a voice-conversion (VC) system to mimic professional
announcers’ voices by modifying the speaker embedding of nonexpert speech. The results from our experiments to evaluate thissystem indicated that intelligibility increased significantly withthis system. In this paper, to discuss what physical features
correlate to the intelligibility, the following two issues are investigated by analyzing this system: (1) whether speech intelligibility
can be changed gradually even by shifting one PCA (principal
component analysis) component of the speaker embedding of theabove VC system and (2) what physical features are changed
when the PCA component is shifted, we retrained the VC systemwith a larger amount of training data. Comparing the speech
intelligibility and candidate features that were changed with theshift of one axis of PCA, we found that spectral tilt, spectral
plateau, and cepstral peak prominence are strongly correlatedwith intelligibility.

Keywords: spectral tilt, spectral plateau, cepstral peakprominence, PCA, STOI, voice conversion
I. I NTRODUCTION
Speech intelligibility is important in providing general information such as the arrival/departure time of trains and flights;
driving instructions, nearby gas stations, and parking lots. It
is especially important in announcing emergency situations
such as fires, earthquakes, and landslides; as well as guiding
people to safety. Making speech more intelligible is specificallychallenging in noisy and reverberant environments such as
airport/train stations, kitchens, and shower rooms. It is evenmore difficult when listeners are elderly with or without
hearing impairments.
The easiest and most natural response to the challenges isturning up the volume of playback devices; however, increasing the volume is not equivalent to increasing the intelligibilityof speech. A person can involuntarily increase the intelligibilityof his/her speech in noisy places, known as the Lombard effect[1]. But increasing the volume of playback devices does nothave such effect. Various approaches have been proposed toincrease speech intelligibility in noisy environments without
increasing the total power of speech. These include modification of spectral properties [2]–[4], dynamic range compression[5]–[7], modification of speech modulation spectrum [8], [9],and time-scale modification [10], [11].
Several studies have found that voice-related professions,e.g., professional announcers, voice actors, and singers, canproduce speech that is clearer and easier to hear than nonprofessional people [12], [13]. Other studies have shown thatthe speech by professional announcers can maintain intelligibility better than that by non-experts in very noisy environments [14]. Inspired by this phenomenon, a previous study [15]developed an end-to-end deep neural network (DNN) based
voice conversion (VC) system that mimicked the speaking styleof professional announcers to increase speech intelligibility of
non-expert people.
As reported in the previous study [15], by analyzing theprincipal component analysis (PCA) of speaker embeddingin the proposed VC system, the first PCA component was
found to be related to gender difference and the second PCA
component of speaker embedding captured the difference between non-expert speakers and professional announcers. Thisis advantageous as a non-expert voice can be converted tohave the voice style of a professional announcer only bychanging the second PCA component of a speaker embedding.
Since professional announcers’ voices are more intelligible
even in a noisy environment, it is expected that the second
PCA component can be used to increase the intelligibilityof non-expert voices. To clarify this point, the authors [15]proposed replacing the second PCA component of a nonexpert speaker embeddings with the average value calculated
from the second principal component of all male professional
announcers. The obtained speaker embedding was then used
to synthesize converted stimuli.
Experimental results [15] from objective measurements andsubjective evaluation of the converted stimuli confirmed that
adapting to an announcer’s voice can increase the intelligibilityof a non-expert speaker’s voice. By modifying the second PCA
component of speaker embedding, we can manually control theannouncer speaking style, hence increase the intelligibility ofspeech in a noisy environment without completely changing
speaker individuality. Statistical analysis also showed thatmodifying the second PCA component yields the highest
performance. These results suggest that the second PCA com-

Female announcer
Male announcer

Female non-expert
Male non-expert

Second PCA Component

4
2

M105

0
Center2
4
Fig. 1.

2
02First PCA Component

4

Speaker embeddings in PCA domain

ponent is related to the intelligibility of speech.
However, the DNN-based VC system is a black box; it
is unknown what happens that improves the intelligibility ofspeech when the second PCA component is swapped. It isalso unclear whether speech intelligibility can be changed
gradually by shifting only the second PCA component. This
paper analyzes speaker embeddings in the system [15] that isthought to be a black box in a DNN-based VC for improving
speech intelligibility and discusses physical cues for improving
intelligibility. More specifically, we retrain the above VC
system with a larger amount of training data and use it
to control the second PCA component. We then study thesubsequent effects on physical properties and intelligibility ofspeech and present two discussion points:• Whether speech intelligibility can be changed graduallyeven by shifting one PCA component of the speakerembedding.
• What physical features are changed when the PCA component is shifted.
Possible candidates of physical correlates are spectral tilt andplateau and cepstral peak prominence (CPP).
II. VOICE - CONVERSION SYSTEM
A. Training voice-conversion system
StarGANv2-VC [16] is one of the most effective VC systems. On the basis of this system, the authors [15] proposed amethod for converting the voice of a non-expert speaker to thatof a professional announcer to increase speech intelligibility.
Their goal was to increase the effectiveness of StarGANv2-VC
by adding automatic speech recognition to the discriminator

module. They also used a Parallel WaveGAN [17] vocoder togenerate converted speech utterances from the corresponding
converted mel-spectrograms.
We retrained the above VC system with a larger amountof training data to discuss the properties of the PCA second
component of the speaker embedding related to expert versus
non-expert speakers. More specifically, the training data consistof utterances from 20 professional announcers from the ATR
dataset A-set and 20 non-expert speakers from the ATR dataset
C-set [18]. All the utterances are pre-processed by resampling
to 24 kHz, removing leading and trailing silence; and they
are combined to 5-second chunks. There are total 29,956
utterances, in which 500 utterances are used for validation.
The 80-band log-mel spectrogram with band-limited frequency
range (0 to 8 kHz) is extracted using short-time Fouriertransform. The window length and frame shift are set to 1024
and 256, respectively.
We follow the training strategy described in the above
studies [15], [16] with the same objective functions and hyperparameters. We then used the style encoder of our retrained
system to generate the PCA of the speaker embeddings for
non-experts and professional announcers to verify that thesecond PCA component is related to expert and non-expert
speakers.
Figure 1 shows the visualization of the first two PCA
components of the speaker embedding. It can be seen that thefirst PCA component is related to gender with female and malespeakers distributed on the left and right. The second PCA
component is related to expert and non-expert speakers. The
clusters of female/male professional announcers are compact
and those of non-expert speakers are wide on the second PCA
component. This phenomenon suggests that the professional
announcers can control the configurations of their voice organs
and high intelligible speech is the result of such effort, resultingthat the variance of their voices are smaller than those of nonexpert speakers.
B. Shifting second PCA component
We referred to the second PCA component of a nonexpert speaker (M105 in the ATR dataset C-set) as the source
and average value of the second PCA component of ten
professional male announcers in the ATR dataset A-set as theCenter. The PCA components of M105 and the Center are
visualized in Fig. 1. Let d be the distance between the PCA
values (the second PCA component) of the source and Center.
We calculated three checkpoints, i.e., C1 = 0.25d, C2 = 0.5d
and C3 = 0.75d from the source, respectively. For example,
assuming that the second PCA components of the source andCenter were vectors: a and b; the checkpoints were calculated
as: C1 = 0.25d = (1 − 0.25) × a + 0.25 × b, C2 = 0.5d = (1 −0.5)×a+0.5×b, and C3 = 0.75d = (1−0.75)×a+0.75×b.
With our retrained voice-conversion system, we first calculated the PCA components for a non-expert speaker andall ten male professional announcers. We then calculated theCenter by taking the average of the second PCA components
corresponding to the professional announcers and calculated

0.7
-9 dB

-6 dB

-3 dB

0 dB

0.6

eSTOI

0.5
0.4
0.3
0.2
0.1
0

0

0.25

0.5

0.75

1

Second PCA Component
Fig. 2. eSTOI scores at four levels of noise (-9, -6, -3, 0 dB). On thehorizontal axis, 0 denotes the source and 1 is the Center. 0.25, 0.5 and 0.75
are the checkpoints, C1, C2, and C3, respectively.

the distance between the second PCA components of the nonexpert speaker and the Center as d. We then used d to calculate
the PCA values for checkpoints C1, C2, and C3 accordingly.
Finally, in turns, we replaced the second PCA component of
the non-expert speaker embedding with the PCA values of C1,
C2, C3, and Center and used our retrained voice-conversion
system to produce the converted speech stimuli.
III. S ECOND PCA COMPONENT AND INTELLIGIBILITY IN
NOISE

The first discussion point is investigated in this section. We
conducted an experiment to gain insights about the correlation
between the second PCA component and speech intelligibility.
The consistency of the correlation was also examined invarious levels of noise.
We selected 520 Japanese words, each containing 1 to 4
morae, from the ATR dataset A-set and ATR dataset C-set as
the clean stimuli for conversion-target and source speakers. All
speech waveforms were pre-processed to a 16-kHz sampling
rate with a single channel. We followed the instructions in[15] and used 16 kHz here instead of 24 kHz as describedin Section II. There were 6 types of speech stimuli in theexperiments: SN is the natural speech of a non-professional
speaker, collected from speaker M105 in the C-set; SA is thenatural speech of professional announcers, collected from 20
male and female speakers in the A-set; and S1 , S2 , S3 , and SC
are converted speech produced by shifting the second PCA
components of speaker embedding of the source (M105) tocheckpoint C1, C2, C3, and Center, respectively.
To create the noisy stimuli, we masked the clean stimuli
with pink noise at four different signal-to-noise-ratio (SNR)
levels: -9, -6, -3, and 0 dB. We calculated the root-mean-squareof the speech signal only in the speech-presence region andscaled the noise signal to match the desired SNR level. The
speech-presence region is derived from the text labels of the
ATR dataset. To avoid the effect of different onset and offset
timing between speech stimuli, the duration of each stimulus

was adjusted to contain the same 200 ms of leading noiseand 200 ms of trailing noise. Speech stimuli were gated with
two raised cosine onset and offset windows of 40 ms to avoid
overshoot distortion.
To objectively measure the intelligibility of speech in noiseand determine whether speech intelligibility is increased gradually by shifting values along the second PCA component,
we calculate the extended Short Term Objective Intelligibility(eSTOI) of the speech stimuli at 4 SNR levels: -9, -6, -3 and0 dB at 5 checkpoints (Source, C1, C2, C3, and Center) using
pySTOI python package [19]. The clean speech by speaker
M105 were used as the reference signals at checkpoint 0 andthe clean-converted speech were used as the reference signals
at other checkpoints for eSTOI calculation.
Figure 2 shows the experimental results. The vertical lines
at each checkpoint correspond to the standard deviations ofeSTOI scores. It appears that intelligibility was increased
gradually when the second PCA component was shifted fromsource to checkpoints C1, C2, C3, and Center. The eSTOI
scores were also consistent with various levels of noise. The
results provide an important insight in that the intelligibility
could be tuned adaptively to various levels of noise on the basisof the voice of an arbitrary person by shifting the second PCA
component using our retrained system.
IV. I NTELLIGIBILITY AND ITS PHYSICAL CORRELATES
A. Spectral tilt, spectral plateau, and intelligibility
Studies on laryngeal anomalies have found that breathinessis a prominent feature used for determining various pathological conditions [20]. Breathiness is a phenomenon caused by the
incomplete closure of vocal fold. As a result, breathy voices are
more difficult to hear (less intelligible) in noisy environments.
Compared with normally phonated signals, breathy glottal
signals have steeper downward spectral slopes, less energy inthe high-frequency region, and are less periodic [21]. Several
acoustical studies have reported correlations between spectral
tilt (downward spectral slope), spectral plateau (energy in thehigh-frequency region); and breathiness ratings [22]. Based onthese knowledge, we hypothesize that a speech signal that has aflatter spectral tilt and larger plateau energy could have higher
intelligibility and vice versa.
Figure 3 (a) shows an example of the spectral tilt we
calculated for this study. It is the frequency spectrum of thevowel /a/ with the spectral tilt presented as the red line. We
used the label of the ATR dataset to extract a segment of vowel/a/. We then calculated the Fourier spectrum of the segment.
Finally, we calculated the regression line of the spectrum
between 300 and 4,000 Hz. We used the slope of the regression
line as the spectral tilt. Figure 3 (b) shows an example of thespectral plateau. On the same spectrum shown in Fig. 3 (a),the spectral plateau is presented as the red line between 2,000
and 4,000 Hz. We subtracted the entire spectrum from its peakmagnitude. We then calculated the average magnitude of thespectrum in the 2,000-4,000-Hz band. We used this averagemagnitude to represent the spectral plateau and compared itwith the plateaux of other spectra.

0.02

20
0
-20
0

0

2000

4000

6000

8000

10000

0

Spectral Tilt (dB/Hz)

Magnitude (dB)

(a)
40

-0.02

-0.04

-0.06

(b)

Magnitude (dB)

-0.08

0

0.25

0.5

0.75

1

Second PCA Component-50

Fig. 4. Average spectral tilt and standard deviation of vowels /a/, /e/, /i/, /o/,/u/. On the horizontal axis, 0 denotes the source and 1 is the Center. 0.25, 0.5
and 0.75 are the checkpoints, C1, C2, and C3, respectively.
-100

0

2000

4000

6000

8000

-25

10000

Frequency (Hz)

Magnitude (dB)

(c)

50

0

0

100

200

300

400

500

Quefrency (ms)Fig. 3.

Frequency spectrum and quefrency cepstrum of vowel /a/

B. Periodicity, CPP, and intelligibility
A breathy voice is less periodic compared with a normal
one and the quefrency domain is effective for analyzing signal
periodicity. A crude explanation of the quefrency domain isthat we treat the spectrum of a speech waveform as a signal
and apply Fourier transform to it; the result is the cepstrumof the speech waveform in the quefrency domain. It can beobserved that a highly periodic speech spectrum should resultin clear harmonics in the quefrency domain; therefore, clearer
cepstral peaks.
Several analyses have found a strong relation between the
periodicity of a signal and its CPP [20], [23]. A CPP is the
distance between the most prominent peak and the level of the
cepstral background noise on the regression line immediately
below the peak. Experimental results [20]–[23] indicated that
the CPP of a breathy voice is significantly lower than that of anormal voice. In other words, a higher CPP level is an indicatorof higher speech intelligibility. Figure 3 (c) shows an exampleof the CPP in the quefrency domain. It is the cepstrum of thevowel /a/ with its regression line and CPP shown as the redline and red triangle, respectively. We use the regression line of
the cepstrum as the referenced background noise. The CPP is

Spectral Plateau (dB)

-30

100

-35
-40
-45
-50
-55

0

0.25

0.5

0.75

1

Second PCA Component
Fig. 5. Average spectral plateau and standard deviation of vowels /a/, /e/,/i/, /o/, /u/. On the horizontal axis, 0 denotes the source and 1 is the Center.
0.25, 0.5 and 0.75 are the checkpoints, C1, C2, and C3, respectively.

calculated as the difference in magnitude of the highest peak(the red triangle) and the red circle directly under it on the
regression line.
C. Second PCA component, physical correlates, and intelligibility
Thus far, we have learned from the literature that a shallower
spectral tilt, larger amount of energy of the spectral plateau,
and higher CPP are associated with higher speech intelligibility. The next step is examining the correlation between thesecond PCA component and acoustical properties. We shifted
the second PCA component, as described in Section II-B, andused our retrained voice conversion system to generate five
types of stimuli. We then used the ATR dataset label to extract
the vowel /a/, /e/, /i/, /o/, /u/ segments of five types of stimuli(SN , S1 , S2 , S3 , and SC ). Finally, we used these segments tocalculate spectral tilt, spectral plateau, and CPP.
The results of this experiment are shown in Figs. 4, 5, and 6.
The vertical lines at each checkpoint correspond to the standard
deviations of the averaged spectral tilts, spectral plateaux, andREFERENCESREFERENCES

32

CPP values becomes larger, indicating that the intelligibility
becomes higher when the second PCA component is shiftedfrom the non-expert speaker toward the Center.
The experimental results visualized in Fig. 2 show thatspeech intelligibility is increased gradually when the second
PCA component is shifted gradually. The shift also produces
enhancements of spectral tilt, spectral plateau, and CPP as
shown in Figs. 4, 5, and 6, respectively. These results provide
evidence that there is a correlation between the improvementof intelligibility and enhancement of physical properties ofspeech.

30

CPP (dB)

28
26
24
22
20

0

0.25

0.5

0.75

1

Second PCA Component
Fig. 6. Average cepstral peak prominence and standard deviation of /a/, /e/,/i/, /o/, /u/. On the horizontal axis, 0 denotes the source and 1 is the Center.
0.25, 0.5 and 0.75 are the checkpoints, C1, C2, and C3, respectively.

CPPs. It is known that a breathy voice (low intelligibility) has
a steeper spectral slope (steeper tilt) and less energy in thehigh-frequency region (small or no plateau). It can be seen in
Fig. 4 that the averaged slopes of the spectral tilts are reduced
gradually from about −0.05 dB/Hz at checkpoint 0 to above−0.02 dB/Hz at the Center. Similarly, it appears in Fig. 5that the average energy in the high-frequency regions (spectral
plateaux) are rised from about −47 dB at checkpoint 0 toabout −33 dB at the Center. Observing the experimental results
visualized in Figs. 4, 5, and 2, there is a relation between
the spectral tilt, spectral plateau, and intelligibility, i.e., theimprovement of these physical properties is consistent with
the improvement of speech intelligibility. More specifically, thenon-expert speaker has the steepest spectral tilt and smallest
spectral plateau, suggesting that the non-expert speaker has thelowest speech intelligibility. In contrast, the Center appears tohave the shallowest spectral tilt and largest spectral plateau;
therefore, it has the highest intelligibility. Between the nonexpert speaker and Center, checkpoints C1, C2, and C3 havea small fluctuation; however, the overall trend is that the
spectral tilts become shallower and spectral plateaux larger,
indicating that the intelligibility becomes higher when thesecond PCA component is shifted from the non-expert speaker
to checkpoints C1, C2, C3, and finally the Center.
It has been reported that the spectrum of a breathy voice (lowintelligibility) is less periodic, and a less periodic spectrum
results in a less prominent cepstral peak. It can be seen in
Fig. 6 that the averaged cepstral peaks have become more
prominent, rising from about 25 dB at checkpoint 0 to above 28
dB at the Center. Observing the experimental results visualized
in Figs. 6 and 2, there is a relation between the CPP and
intelligibility, i.e., the improvement of the CPP is consistent
with the improvement of speech intelligibility. More specifically, the non-expert speaker has the lowest CPP; in otherwords, the speaker has the least speech intelligibility. The CPP
values of other checkpoints are all larger than that of the nonexpert. Although there is a small fluctuation, it appears that the

V. C ONCLUSION
We presented two points of discussion for finding thecorrelations between the second PCA component, physicalproperties, and intelligibility of speech in order to learn why
speech intelligibility can be increased by mimicking professional anouncers’ voices using the end-to-end DNN-based VC
system [15]. The first point is whether speech intelligibility
can be changed gradually even by shifting one second PCA
component of the speaker embedding and the second point
is what physical features are changed when the second PCA
component is shifted. To do so, we retrained the VC system[15] with a larger amount of training data. We then used ourretrained system to shift the second PCA component of a nonexpert speaker to those of checkpoints C1, C2, C3, and theCenter. We used the eSTOI to evaluate the intelligibility ofspeech stimuli with four levels of noise (-9, -6, -3, and 0 dB)
at five checkpoints. Experimental results confirm for the firstpoint of discussion that speech intelligibility can be increased
gradually by shifting the second principal component toward
those of professional announcers.
We investigated why it is possible to increase speech intelligibility by using the second PCA component of professional
announcers. We looked into the spectral tilt and spectral
plateau in the frequency domain, as well as the CPP in thequefrency domain. The results confirm for the second pointof discussion that when the second PCA component is shiftedfrom a non-expert speaker through checkpoints C1, C2, C3,
and Center, all the acoustical properties related to intelligibility
appear to be enhanced proportionally.
VI. ACKNOWLEDGEMENTS
This work was supported by the SCOPE Program of Ministry of Internal Affairs and Communications (Grant number:
201605002) and Grant-in-Aid for Scientific Research (Grant
number: 20H04207).
R EFERENCES[1]

H. Brumm and S. A. Zollinger, “The evolution of theLombard effect: 100 years of psychoacoustic research,”
Behaviour, vol. 148, no. 11/13, pp. 1173–1198, 2011.
[2] W. B. Kleijn, J. B. Crespo, R. C. Hendriks, P. Petkov,
B. Sauert, and P. Vary, “Optimizing speech intelligibility
in a noisy environment: A unified view,” IEEE Signal
Processing Magazine, vol. 32, no. 2, pp. 43–54, 2015.
DOI : 10.1109/MSP.2014.2365594.

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

C. H. Taal and J. Jensen, “SII-based speech preprocessing for intelligibility improvement in noise,” in Proc.
Interspeech 2013, 2013, pp. 3582–3586. DOI: 10.21437/
Interspeech.2013-770.
C. H. Taal, R. C. Hendriks, and R. Heusdens, “Speechenergy redistribution for intelligibility improvement innoise based on a perceptual distortion measure,” Computer Speech and Language, vol. 28, no. 4, pp. 858–
872, 2014, ISSN: 0885-2308. DOI: https : / / doi . org /10 . 1016 / j . csl . 2013 . 11 . 003. [Online]. Available:
https : / / www . sciencedirect . com / science / article / pii /S0885230813001101.
T.-C. Zorilă and Y. Stylianou, “On spectral and time
domain energy reallocation for speech-in-noise intelligibility enhancement,” in Proc. Interspeech 2014, 2014,
pp. 2050–2054. DOI: 10.21437/Interspeech.2014-466.
T.-C. Zorila, V. Kandia, and Y. Stylianou, “Speechin-noise intelligibility improvement based on spectral
shaping and dynamic range compression,” in Proc.
Interspeech 2012, 2012, pp. 635–638. DOI: 10.21437/
Interspeech.2012-197.
C. Chermaz and S. King, “A Sound Engineering Approach to Near End Listening Enhancement,” in Proc.
Interspeech 2020, 2020, pp. 1356–1360. DOI: 10.21437/
Interspeech.2020-2748.
A. Kusumoto, T. Arai, K. Kinoshita, N. Hodoshima, andN. Vaughan, “Modulation enhancement of speech bya pre-processing algorithm for improving intelligibility
in reverberant environments,” Speech Communication,
vol. 45, no. 2, pp. 101–113, 2005, ISSN: 0167-6393.
DOI : https://doi.org/10.1016/j.specom.2004.06.003.
[Online]. Available: https : / / www. sciencedirect . com /science/article/pii/S0167639304000597.
T. Ngo, R. Kubo, and M. Akagi, “Increasing speech
intelligibility and naturalness in noise based on concepts of modulation spectrum and modulation transfer
function,” Speech Communication, vol. 135, pp. 11–24,
2021, ISSN: 0167-6393. DOI: https : / / doi . org / 10 .
1016 / j . specom . 2021 . 09 . 004. [Online]. Available:
https : / / www . sciencedirect . com / science / article / pii /S016763932100100X.
Y. Tang and M. Cooke, “Subjective and objective
evaluation of speech intelligibility enhancement underconstant energy and duration constraints,” in Proc. Interspeech 2011, 2011, pp. 345–348. DOI: 10 . 21437 /Interspeech.2011-126.
V. Aubanel and M. Cooke, “Information-preserving
temporal reallocation of speech in the presence offluctuating maskers,” in Proc. Interspeech 2013, 2013,
pp. 3592–3596. DOI: 10.21437/Interspeech.2013-772.
H. Noh and D.-H. Lee, “How does speaking clearly influence acoustic measures? a speech clarity study using
long-term average speech spectra in korean language,”
Clinical and Experimental Otorhinolaryngology, vol. 5,
pp. 68–73, 2012.

[13]

[14]

[15]

[16]

[17]

[18]
[19]
[20]

[21]

[22]

[23]

C. Kashimada, K. Ogita, T. Ishikawa, H. Hasegawa,and M. Ayama, “Effects of voice training on subjective
evaluation of voice quality,” The Journal of The Instituteof Image Information and Television Engineers, vol. 63,
no. 12, pp. 1818–1823, 2009. DOI: 10.3169/itej.63.1818.
M. Kobayashi and M. Akagi, “Intelligibility of announcer‘s speech in noisy environments,” IEICE Technical Report, vol. 119, pp. 95–99, 2020.
T. Vu Ho, M. Kobayashi, and M. Akagi, “Speak Like a
Professional: Increasing Speech Intelligibility by Mimicking Professional Announcer Voice with Voice Conversion,” in Proc. Interspeech 2022, 2022, pp. 171–175.
DOI : 10.21437/Interspeech.2022-124.
Y. A. Li, A. Zare, and N. Mesgarani, “StarGANv2VC: A Diverse, Unsupervised, Non-Parallel Framework
for Natural-Sounding Voice Conversion,” in Proc. Interspeech 2021, 2021, pp. 1349–1353. DOI: 10.21437/
Interspeech.2021-319.
R. Yamamoto, E. Song, and J.-M. Kim, “Parallel wavegan: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram,” in ICASSP 2020 - 2020 IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 6199–6203. DOI: 10 . 1109 /ICASSP40776.2020.9053795.
A. T. R. I. International, Digital voice database, http://www.atr-p.com/.
P. Manuel, Python implementation of STOI, https : / /github.com/mpariente/pystoi.
J. Hillenbrand, R. A. Cleveland, and R. L. Erickson,“Acoustic correlates of breathy vocal quality,” Journalof Speech, Language, and Hearing Research, vol. 37,
no. 4, pp. 769–778, 1994. DOI: 10.1044/jshr.3704.769.
H. M. Hanson and E. S. Chuang, “Glottal characteristics
of male speakers: Acoustic correlates and comparison
with female data,” The Journal of the Acoustical Societyof America, vol. 106, no. 2, pp. 1064–1077, 1999. DOI:
10.1121/1.427116.
E. B. Holmberg, R. E. Hillman, J. S. Perkell, P. C.
Guiod, and S. L. Goldman, “Comparisons among aerodynamic, electroglottographic, and acoustic spectral
measures of female voice,” Journal of Speech, Language, and Hearing Research, vol. 38, no. 6, pp. 1212–
1223, 1995. DOI: 10.1044/jshr.3806.1212.
R. Fraile and J. I. Godino-Llorente, “Cepstral peakprominence: A comprehensive analysis,” Biomedical
Signal Processing and Control, vol. 14, pp. 42–54,
2014, ISSN: 1746-8094. DOI: https : / / doi . org / 10 .
1016 / j . bspc . 2014 . 07 . 001. [Online]. Available:
https : / / www . sciencedirect . com / science / article / pii /S1746809414000986.