Keywords
voice - voice quality - mobile phone
Introduction
Mobile phones have become an integral aspect of contemporary social life. The inability
to use a phone can significantly impact daily living, potentially restricting job
opportunities. Communication via phone poses a unique challenge for individuals with
hearing loss, including those with cochlear implants and traditional hearing aids.[1]
[2] However, there is a scarcity of literature investigating the specific reasons for
this challenge.
Voice interactions and voice messaging on mobile phones are experiencing rapid growth
in popularity. Consequently, there is a rising emphasis on enhancing voice message
technology.[3] Some studies have suggested that wireless connections between phones and hearing
aids can enhance speech intelligibility.[4]
[5]
[6]
[7] In studies examining the acoustic quality of mobile phone voice recordings, some
authors have asserted that smartphones' measurements of acoustic voice parameters
are reliable in clinical settings, while others have contested this viewpoint.[8]
[9]
[10] Studies demonstrate that sound frequencies can be reliably measured using mobile
phone applications.[11] The contradictory results in these studies may stem from methodological differences,
such as the type of recording devices used, the recording environments, and the analysis
techniques. Some studies employed high-quality clinical microphones, while others
used smartphone microphones or mobile communication devices. The recording conditions
varied, with some conducted in anechoic chambers and others in standard clinical or
noisy environments. Additionally, the inclusion of onset/offset segments, different
software for acoustic analysis, and variations in participant populations may have
contributed to discrepancies in results. Notably, these studies have not delved into
the transmission of voice to the receiving party. However, the exact way voicemail
apps process and transmit sound remains unclear, potentially due to their specific
coding methods.
Research has shown that telephone signal transmission significantly alters the acoustic
properties of voice, affecting both objective measurements and perceptual evaluations.
Commonly observed issues include amplitude distortion and frequency response modifications,
which impact speech clarity and overall quality.[12]
[13] Interestingly, the mean fundamental frequency remains relatively stable across different
telepractice platforms.[14] Additionally, perceptual assessments of voice quality are influenced by telephone
transmission.[13]
[15] As a result, telephone transmission introduces various acoustic distortions that
must be carefully considered, especially in applications like forensic analysis, where
precise voice assessment is crucial. Despite existing research highlighting significant
changes in voice transmitted over the telephone, studies specifically examining objective
alterations remain limited.
This study aims to explore the objective changes in voice recordings caused by mobile
phones and voicemail applications during the transmission process, specifically focusing
on the alterations to voice quality that occur during this process.
Methods
This study was performed at a tertiary health center. The participants comprised 45
volunteer health workers with an average age of 36.7 ± 7.5 (mean ± standard deviation).
All volunteers were male, and their ages ranged between 22 and 50 years to ensure
standardization. Individuals with acid reflux, asthma, allergies, acute respiratory
infections, as well as those who had undergone laryngeal or thyroid surgery, were
excluded from the study. Perceptual voice evaluations were conducted on the volunteers
before the test, and only participants with healthy voices were included in the study.
These evaluations were performed by an otolaryngologist using the GRBAS scale. Participants
were considered to have 'healthy voices' if they received a global severity rating
of 0 (normal) on the GRBAS scale.
Acoustic measures were compared between voice signals simultaneously recorded from
healthy speakers through an iPhone (iPhone 6s model A1303, Apple, USA) and a recording
system with a microphone (Shure SM58; Shure Inc., Niles, IL). The recording was performed
using a laptop computer with a sampling frequency of 44,100 kHz and a resolution of
16 bits in '.nsp' format via the Multi-Dimensional Voice Program (MDVP; Kay Elemetrics,
Lincoln Park, NJ, USA). The mobile phone was placed adjacent to the microphone with
a holder. Voice samples were recorded at ∼10 cm from the lips, directed toward the
mouth at a 45-degree angle, in a soundproof and anechoic room. The speakers were instructed
to produce a steady sustained phonation of the vowel /a/ for ∼5 seconds at habitual
loudness and pitch. Voice breaks were not present in the analyzed segments considered
for evaluation. Three phonations were then recorded, and the one with the best voice
analysis measurement was selected to ensure that any observed changes were due to
mobile phone transmission rather than variability in the original voice production.
The initial and final 20 cycles of each phonation were excluded from the analysis
to avoid perturbations related to the onset and offset of phonation.
Voice recordings through the mobile phone were conducted using the WhatsApp voice
messaging application (WhatsApp Inc., Attn: WhatsApp Copyright Agent, 1601 Willow
Road, Menlo Park, California 94025, USA) and sent to another WhatsApp and iPhone 6s
user. Voicemail recordings were analyzed instead of direct phone call voices to eliminate
voice transmission problems associated with telephone networks. To minimize factors
affecting the acoustic quality of WhatsApp voice messages identical devices and WhatsApp
versions were used. The primary focus of the study was to examine the changes in voice
caused by the mobile phone and voicemail application. Mobile phone recordings in 'mp4'
format were transferred to a Mac computer, and via iTunes software (Apple Inc.), 'p4'
files were converted to '.wav' files to enable analysis with MDVP. The same duration
of voice recording segments was utilized for comparing the free-field and mobile phone
voices.
After voice recordings of all patients were completed, all voice samples were analyzed
with MDVP. Seven clinically significant parameters, which can be grouped under four
headings, were selected for analysis:
Average fundamental frequency (Fo) /Hz/: Average value of all extracted period-to-period
fundamental frequency values.
Pitch period perturbation quotient (PPQ) /%/: Relative evaluation of the period-to-period
variability of the pitch within the analyzed voice sample with a smoothing factor
of 5 periods.
Relative average perturbation (RAP) /%/: Relative evaluation of the period-to-period
variability of the pitch within the analyzed voice sample with a smoothing factor
of 3 periods.
Shimmer in dB (ShdB) /dB/: Evaluation in dB of the period-to-period (very short-term)
variability of the peak-to-peak amplitude within the analyzed voice sample.
Shimmer percent (Shim)/%/: Relative evaluation of the period-to-period (very short-term)
variability of the peak-to-peak amplitude within the analyzed voice sample.
Amplitude perturbation quotient (APQ) /%/: Relative evaluation of the period-to-period
variability of the peak-to-peak amplitude within the analyzed voice sample at the
smoothing level of 11 periods.
Noise-to-Harmonic Ratio (NHR): Average ratio of the inharmonic spectral energy in
the frequency range 70–4200 Hz to the harmonic spectral energy in the frequency range
70–4200 Hz. This is a general evaluation of the noise present in the analyzed signal.
The data were analyzed using the software Statistical Product and Service Solutions
(SPSS), Predictive Analytics Software (PASW), and Statistics 21 (SPSS Inc.; IBM Corp.,
Chicago, IL, USA). A paired student's t-test was applied to the measurements in all groups. A p-value of <0.05 was considered
statistically significant.
Results
Analysis of the patient voice data, comparing the free-field voice and the mobile
phone recordings, revealed that Fo is resistant to alterations in voice (p = 0.313). Frequency perturbation parameters (PPQ, RAP) were affected (p = 0.018, 0.020, respectively); however, amplitude perturbation parameters (ShdB,
Shim, APQ) and the noise parameter (NHR) were significantly impacted in voice transmission
caused by mobile phones (p < 0.001 in all). The mean and standard deviation of acoustic measures for the free-field
and the mobile phone voicemail recordings, along with p values, are summarized in
[Table 1]. A comparison of acoustic measurements between free field and mobile phone voicemail
recordings is illustrated in [Fig. 1].
Table 1
Mean and standard deviation of acoustic measures of the free-field and the mobile
phone voicemail recordings and p values
|
Reference threshold values
|
Free field (mean ± SD)
|
Mobile phone (mean ± SD)
|
p*
|
|
Fundamental frequency parameter
|
|
Fo (Hz)
|
85–155 (male)
|
126,73 ± 25,62
|
126,45 ± 25,67
|
0,313
|
|
Frequency perturbation parameters
|
|
PPQ (%)
|
< 0.5
|
0,35 ± 0,19
|
0,47 ± 0,29
|
0,018
|
|
RAP (%)
|
< 0.5
|
0,34 ± 0,21
|
0,46 ± 0,29
|
0,020
|
|
Amplitude perturbation parameters
|
|
ShdB (dB)
|
< 0.5
|
0,23 ± 0,08
|
0,54 ± 0,11
|
<0,001
|
|
Shim (%)
|
< 3.0
|
2,64 ± 0,95
|
6,19 ± 1,28
|
<0,001
|
|
APQ (%)
|
< 3.5
|
2,04 ± 0,68
|
4,96 ± 0,98
|
<0,001
|
|
Noise parameter
|
|
NHR
|
< 0.2
|
0,13 ± 0,02
|
0,16 ± 0,02
|
<0,001
|
Abbreviations: APQ, amplitude perturbation quotient; Fo, average fundamental frequency;
NHR, noise-to-harmonic ratio; PPQ, pitch period perturbation quotient; RAP, relative
average perturbation; SD, Standard deviation; ShdB, shimmer in dB; Shim, shimmer percent.
* p: Paired t-test.
Fig. 1 Comparison of acoustic measurements of free field and mobile phone voicemail recordings.
Discussion
While a few previous studies have scrutinized the quality of voice recordings made
with mobile phones, the focus on those transmitted to the other party has been lacking.
This limitation has been improved in the current research to some extent. This study
aims to provide a detailed investigation of the objective acoustic changes induced
by mobile phones specifically in voicemail transmission, focusing on the analysis
of the transmitted voice as received by the other party, a topic that has not been
extensively covered in existing research. Since the audio file is transmitted identically
to the receiving party, our primary focus is on the modifications made by the mobile
phone and voicemail application to facilitate voice transmission, particularly app-specific
encoding. Analysis of our speaker data revealed that the fundamental frequency remained
resistant to alterations in voice; however, all other measures of voice parameters
were statistically significantly impacted in voicemail transmissions caused by mobile
phones. Our findings share similarities with those of Maryn et al in certain aspects.[8] They found that “fundamental frequency was resistant to the recording system and
environmental noise, while all other measures were impacted by both the recording
system and noise condition.” In contrast, Uloza et al. demonstrated that “measurements
of acoustic voice parameters using smartphones proved to be reliable in clinical settings”[9]. Grillo et al. also showed that there was no significant variability among voice
measures with different smartphone recordings. However, their study also included
recordings from a head-mounted microphone, which is important because it offers a
more standardized recording, minimizing external noise.[16] Petrizzo et al., in their review of six studies, concluded that “smartphones and
mobile apps have the potential to be valuable tools in voice assessment outside the
clinic”[10].
Our findings indicate that mobile phones cause significant acoustic changes in voicemail
transmission, particularly in frequency perturbation and amplitude perturbation parameters.
While these changes are evident, further research is needed to understand their impact
on voice quality assessment and clinical applications. Comprehension of spoken messages
involves decoding formant information and articulatory acoustics, as well as determining
fundamental frequency and overall voice quality for speaker identification. Although
the fundamental frequency, which remains resistant to recording and voice transmission,
may play a role in understanding the speaker's identity, relying solely on this parameter
is unlikely to fulfill this purpose. Therefore, future studies should explore how
these mobile phone-induced acoustic changes affect the broader aspects of voice perception
and speaker recognition.
Beyond objective changes, mobile phones may also induce perceptual alterations in
sound transmission. Passetti et al.[15] concluded in their study that the acoustic quality of mobile phones impacts accurate
perception and evaluation of voice quality. They further suggested that these alterations
could be relevant in a forensic context. Consequently, the objective changes in human
voice over the phone, investigated in this study, might also bear significance for
forensic issues.
Examining the effects of certain unavoidable technical limitations in this study,
detailing the measures taken to address them, and exploring their impact on the results
may contribute to a more nuanced interpretation of the study outcomes. First, measuring
real-time voice transmissions as received on another device could introduce numerous
unknown and uncontrollable factors. Hence, voicemails were analyzed as an alternative,
with the assumption that this method could minimize the variability of factors. Second,
the study focused solely on voice parameters and did not assess speech. Nevertheless,
it is acknowledged that reducing loudness or eliminating high frequencies from a signal
can impact speech intelligibility, particularly affecting consonants. Third, data
collection occurred under highly favorable conditions, with voice samples recorded
in a soundproof and anechoic room, featuring subjects with normal voices for standardization
purposes. However, mobile phones are commonly used in environments with elevated background
noise. Lastly, voice transmission across different telephone networks and cellular
phone brands may exhibit variability. Additionally, a significant limitation of this
study is the use of the Shure SM58 microphone. This dynamic microphone, while suitable
for live vocal performance, has a non-flat frequency response (midrange boost and
low/high-frequency roll-off) and a slower transient response compared with condenser
microphones. Its limited high-frequency range (50 Hz–15 kHz) may have significantly
affected the acoustic analysis, particularly in capturing breathiness, roughness,
and harmonic-to-noise ratio. We acknowledge the possibility of selection bias inherent
in choosing the phonation with the most optimal acoustic measurements. However, to
isolate and specifically examine the effects of mobile phone transmission on voice
signals, this selection process was deemed essential. Considering these constraints,
it is conceivable that sound transmitted over the phone could be further distorted
in real-world environments. Furthermore, we suggest that future studies investigate
'Cepstral Peak Prominence' measures, as they are more robust in acoustic voice analysis.
Conclusion
The current findings suggest that mobile phones induce notable acoustic changes in
voicemail transmission. Notably, the fundamental frequency remained resistant to alterations
in voice. These results underscore the importance of further research to evaluate
the effects of these acoustic changes on voice quality assessment, speaker identification,
and clinical applications. Moreover, given the study's technical limitations, future
research should investigate real-world conditions to gain a more comprehensive understanding
of mobile phone-induced acoustic modifications.
Bibliographical Record
Halil Erdem Özel. Acoustic Changes in Voicemail Transmission Induced by Mobile Phones.
Int Arch Otorhinolaryngol 2025; 29: s00451810029.
DOI: 10.1055/s-0045-1810029
