Detecting Speech Disfluencies Using Open-Source Tools in Automatic
Feedback Systems for Oral Presentation Training
Willy Mateo
2 a
, Leonardo Eras
1 b
, Giancarlo Carvajal
2 c
and Federico Dom
1,2 d
Information Technology Center, Escuela Superior Politecnica Del Litoral, ESPOL, Guayaquil, Ecuador
Faculty of Electrical and Computer Engineering, Escuela Superior Politecnica Del Litoral, ESPOL, Guayaquil, Ecuador
{wjmateo, leras, gianflor, fexadomi}
Oral Presentation Skills, Filler Words Detection, Filled Pauses Detection, Automatic Presentation Feedback.
In the realm of verbal communication, most common non-clinical speech disfluencies are filler words and filled
pauses, which pose challenges for effective oral presentations. Yet their detection is no easy task. This article
presents the usage of OpenAI’s Whisper for filled pauses and filler words detection in Spanish oral presen-
tations, including on-the-wild usage with undergraduate students. Preliminary results indicate that Whisper
demonstrates promise as a valuable tool to identify a substantial amount of filler words and filled pauses.
Despite areas of improvement, Whisper serves as a diagnostic tool for assessing disfluences in oral communi-
Speech disfluencies are interruptions in the regular
flow of speech (Scott Fraundorf, 2014). These inter-
ruptions may manifest in several forms during speech
ranging from mild interruptions such as filler words,
where the disfluent sound is a complete word (e.g. ok,
like), and filled pauses, where a natural pause dur-
ing speech is vocalized with prolonged vowels (e.g.
uhmm, ehmmm), to speech disorders such as stutter-
ing (G
osy, 2023; Das et al., 2019). While some mod-
erate use of filled pauses and filler words may in fact
increase listener comprehension and add naturalness
to speech (Scott Fraundorf, 2014), it is commonly un-
derstood that increased usage of these disfluencies de-
note hesitation in the speaker and delay comprehen-
sion in the listener (Lo, 2020).
Practice, self-awareness, and feedback are fre-
quently cited as strategies to reduce the occurrence
of speech disfluencies in oral presentations (De Grez
et al., 2009; Alwi and Sidhu, 2013; Das et al., 2019).
Almost all coaching systems for oral presentations
attempt to detect speech disfluencies either in real
time or offline from recordings to provide automatic
feedback to learners. However, obtaining reasonable
levels of detection accuracy is no easy task. Some
systems use phonetic analysis with software such as
Praat (Boersma and Weenink, 2023), a well-tested,
production-ready open-licensed software for speech
analysis and phonetics, to extract filled pauses from
speech, while others use Automatic Speech Recogni-
tion (ASR) to identify filler words or repetitions from
generated transcripts (Zhu et al., 2022). Phonetic
analysis excels at detecting filled pauses but struggles
with repetitions while ASR technologies, normally
trained to ignore disfluencies, typically require costly
annotated data for retraining (Zhu et al., 2022).
In this work, we focus on the detection of the
most common non-clinical speech disfluencies: filler
words and filled pauses (Lo, 2020), using ASR tech-
nologies with open source tools. Specifically, we
tackle on the problem on providing accurate detection
of speech disfluencies in Spanish speaking students in
the context of an oral presentation.
Since 2018 we have been using the Automatic
Feedback Presentation system (RAP for its Span-
ish acronym) as an experimental tool that facilitates
learning of oral presentation skills and since 2019 as
a learning tool embedded in the academic activities
of communication courses and selected engineering
courses (Dom
ınguez et al., 2021). The RAP system
records a student’s oral presentation in a specialized
room and extracts five presentation features: posture,
gaze to the audience, use of filled pauses, voice vol-
ume, and slides legibility (Ochoa et al., 2018). The
Mateo, W., Eras, L., Carvajal, G. and Domínguez, F.
Detecting Speech Disfluencies Using Open-Source Tools in Automatic Feedback Systems for Oral Presentation Training.
DOI: 10.5220/0012622100003693
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 16th International Conference on Computer Supported Education (CSEDU 2024) - Volume 2, pages 213-220
ISBN: 978-989-758-697-2; ISSN: 2184-5026
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
system uses Praat to measure voice volume and de-
tect filled pauses from the audio recording. It uses
this extracted features to generate an interactive and
customized feedback report at the end of the presen-
tation. In 2020, we demonstrated that this facilitates
modest, but statistically significant, learning gains to
their users (Ochoa and Dominguez, 2020).
Aiming to improve the capacity of the RAP sys-
tem to detect speech disfluencies, we implemented
two detection algorithms, based on OpenAI’s Whis-
per (Radford et al., 2022), for filler words and filled
pauses. Section 2 summarizes the state of the art
on the detection of non-clinical speech disfluencies,
section 3 describes the experimental methodology for
evaluation of both algorithms with annotated data and
in a small on-the-wild setup within the RAP system,
section 4 describes the obtained results, and sections 5
and 6 summarize our findings.
The domain of identifying and classifying filler words
in audio recordings is currently evolving and ongo-
ing. A relevant study, conducted in 2022 (Zhu et al.,
2022), focused on the detection and classification of
filler words in English audio recordings. To do this,
audio podcasts collected from the online music plat-
form SoundCloud were used, with a total of 145
hours of recordings. The focus of this research was
based on the creation of a pipeline using Voice Ac-
tivity Detection (VAD) and ASR techniques, which
allowed a more accurate and efficient identification
of filler words present in recordings compared to the
keyword-based approach.
More recent work, conducted in June 2023 (Zhu
et al., 2023), addresses the challenge of detecting non-
linguistic filler words. It is recognized that universal
accessibility to ASR systems may be limited by fac-
tors such as budget constraints, the diversity of target
languages, and the computational resources required.
Through the employment of Structured State Space
Sequence (S4) models and semi-Markov neural con-
ditional random fields (semi-CRF), an absolute per-
formance improvement of 6.4% at the segment level
and 3.1% at the event level is achieved in the Podcast-
Fillers dataset.
As for commercial tools, Microsoft’s ability
to evaluate oral presentations through the Speaker
Coach (Microsoft, 2023) tool within the PowerPoint
application stands out. This tool allows simulations
of oral presentations, providing detailed results on the
quality of the presentation. It evaluates various pa-
rameters such as speech rate, pronunciation, tone of
voice, repetitive language, inclusive language, origi-
nality, and use of filler words. However, this func-
tionality is restricted to the Microsoft PowerPoint en-
vironment, lacking an API to integrate it into external
projects and it does not offer the possibility to down-
load a detailed report of the evaluation results.
In addition, various web-based commercial op-
tions offer similar functionalities, each with specific
approaches and payment plans:
Kapwing (Eric and Julia, 2023) introduces the
Smart Crop tool, which detects filler words al-
lowing users to remove these audio or video seg-
Podcastle (Team, 2022) offers the Filler Word De-
tection tool which provides the audio transcrip-
tion, detecting filler words, and allowing users to
delete the specific segment.
Cleanvoice AI (Adrian, 2023) specializes in re-
moving unnecessary sounds, such as filler sounds,
stuttering and mouth noises.
The tools described in the previous section are ei-
ther experimental or commercial. Aiming to integrate
our findings in the RAP system, we focused on open-
source solutions.
3.1 Filler Words
For detecting filler words out of speech transcriptions
using Speech to Text (STT) technology, two open-
source solutions stand out: Whisper and Coqui STT.
Figure 1: Process of filler word detection.
Whisper (Radford et al., 2022) is a model spe-
cialized in speech processing including multilingual
speech recognition, speech translation, spoken lan-
guage identification and voice activity detection. It
has an architecture based on Transformer sequence-
to-sequence and was trained on an extensive dataset
comprising 680 000 hours of audio in 97 languages,
CSEDU 2024 - 16th International Conference on Computer Supported Education
including Spanish. This training resulted in the cre-
ation of 5 models variants, presented in ascending
order from lowest to highest capacity: Tiny, Base,
Small, Medium and Large.
The Medium model was used to identify filler
words. There was no need to apply conversion or
cleanup techniques to the audio as Whisper internally
splits the audio conveniently to deliver optimal re-
3.1.1 Detection and Counting of Filler Words
Previously, communication courses teachers from Es-
cuela Superior Polit
ecnica del Litoral (ESPOL®)
shared with us a list of filler words most frequently
used by students in an oral presentation. With this in-
formation as a foundation, a dictionary was built with
common fillers words as keys and a list with the vari-
ations of said filler word as values.
We noticed that when using Coqui STT it was dif-
ficult to detect certain filler words, so, a boost was
added to those words. In addition, it was unable to
detect composed filler words, for example: ”c
omo es
que es”, limiting its applicability.
At this point, we proceeded to either generate the
transcription of all the audio chunks for Coqui STT, or
to process the entire audio and obtain the transcription
of each segment if Whisper was used.
To save the the number of filler words detected in
an audio file it uses JSON files, this is an open stan-
dard file format used to store and transmit data ob-
jects consisting of attribute–value pairs and arrays. A
JSON file is created with filler words as keys, with
all values set to 0, representing the number of times
each filler word was detected. Additionally, another
JSON is initialized with the same keywords, assign-
ing empty arrays as values that will store the specific
moments in which each filler word was uttered.
Next, the dictionary of common filler words is it-
erated in descending order by the number of words in
each filler word. In each iteration, by means of the
python library focused on NLP (Natural Lan-
guage Processing), the transcription tokens are ex-
tracted to create a list of n-grams with size equal to the
number of words and obtain their frequency distribu-
tion, this is useful to count the number of occurrences
of the filler word and its variations in the transcription
(Bird et al., 2009).
The JSON is then updated with the number of
filler words detected. In the case of Whisper, the
start and end seconds when each filler word was de-
tected, obtained from the corresponding metadata, are
incorporated. Finally, the filler word and its variations
are removed from the transcription. This procedure
avoids consideration of these words in future itera-
tions, preventing them from being intertwined with
terms from different filler words.
3.1.2 Tool Comparison and Performance
To calculate the performance of each STT technology
to detect filler words, it was necessary to perform the
manual transcription of each test audio.
We tailored a custom metric called Filler Word
Error Rate (FWER), based on the Word Error Rate
(WER) formula which is a commonly used measure
in the evaluation of speech recognition or machine
translation systems. FWER takes into account both
filler word insertions and deletions in the output of
STT technology compared to the total number of filler
words in the manual transcription. So, we calcu-
lated the FWER for each of the test audios using both
Whisper and Coqui STT.
insertions + deletions
f iller words in manual transcript
3.2 Filled Pauses
To correctly capture filled pauses, we rely on the defi-
nition presented by (Mar
ıa J. Machuca, 2015), which
states that the /e/ and /m/ phonemes are the only ones
that can constitute true filled pauses in Spanish oral
speeches. We then define a filled pause as a word that
doesn’t belong to the speech and fits one of the /e/,
/a/ or /m/ phonemes. We complete this definition by
adding prolongations, which are the prolonged sounds
of a vowel (and sometimes a consonant) at the end of
a word. If these prolongations are long enough, they
effectively function as filled pauses. From now on, the
term filled pause will refer to this specific combined
3.2.1 Whisper Setup
Our filler word detection algorithm relies on the
Whisper Small model, rather than the Medium one.
While we initially used both models, we found that
the Small model consistently produced more accurate
results in several comparisons. As a result, we made
the decision to exclusively use the Small model go-
ing forward. We used the transcribe function,
with the temperature value set to 0 to make the
transcription process deterministic, and a string in the
initial prompt parameter.
Detecting Speech Disfluencies Using Open-Source Tools in Automatic Feedback Systems for Oral Presentation Training
3.2.2 Detection and Counting of Filled Pauses
By default, Whisper models may leave out filled
pauses, so we used prompting
to avoid this. The
presence of ellipses (...) plays a very important role
in prompting. The following string is passed in the
prompt parameter because it showed the
best results:
Eh... Rust es... un lenguaje
eh... de programaci
on eh... que
ah... compite eh.. directamente
con... ah... C
We split the original audio in 10 seconds clips. For
each clip, we call the transcribe function and it-
erate trough every word of the transcript, using the
following regular expression to capture filled pauses:
It fits the first part of the provided filled pause
definition and effectively matches strings as “eh...,
“ah..., “mmm..., which represent common Spanish
filled pauses.
For the second part of the definition, we use this
regular expression, as the ellipses represent prolonga-
tions themselves:
Due to the fact that intonation influences whether
a prolongation is considered a filled pause and that
we do not analyze the audio itself, but the transcript
provided by Whisper, we need to minimize the pres-
ence of false positives when capturing prolongations.
We achieve this by adding a time threshold of 0.95
seconds to make sure that the prolongations are true
filled pauses most of the time.
When a filled pause (fp) is detected, two discard
conditions are checked, due to the possibility of the
models entering a repetition loop and transcribe a
word more than once. If the detected filled pause
meets either of them, we ignore it:
The detected fp has the same end timestamp than
the previous one
The detected fp is identical to the previous one
Finally, we increase the fp counter and use the
word timestamps to export an audio clip containing
the filled pause with pydub.
3.2.3 Performance Analysis
Since we only rely on accuracy and recall, we use the
F-score and the average of false positives as the per-
formance analysis metrics for this algorithm. We also
calculate these metrics for the existing Praat-based
filled pause detector to compare.
3.3 Processing Pipeline of Speech
Disfluencies Detection in the RAP
The RAP system is designed to efficiently handle me-
dia recordings and presentation slides. Once a pre-
sentation is completed, the system bundles the media
recordings, presentation slides, and metadata into a
compressed zip file. This file is then transmitted over
the network to a backend server repository using a re-
liable file transfer protocol. The backend server uses
a broker that implements a publish-subscribe mes-
sage protocol to facilitate communication and orches-
tration between all the components of the RAP sys-
tem. Upon arrival at the backend, the broker notifies
a file-consumer service, which decompresses the zip
file and notifies other media-specific services that the
media files are ready for processing. This entire pro-
cess is managed through the broker, ensuring seam-
less coordination between all the components of the
RAP system.
Two services process the audio file sequentially:
the first one to detect and count filler words and gen-
erate a bar chart; the second one to detect and count
filled pauses.
Finally the generated bar chart, along with the au-
dio snippets, are sent to a web service where it be-
comes part of a report for both the student and the
professor. Figure 2 illustrates the overall process pre-
viously described. Figure 3 illustrates the audio file
process previously described.
Figure 2: Complete process of the audio file from the
recording room to the analyzers and finally to become part
of the presentation report.
CSEDU 2024 - 16th International Conference on Computer Supported Education
3.4 On-the-Wild Evaluation Setup
As described in the previous section, we added the
detection of filler words as an experimental feature
in the RAP system to evaluate this feature “on-the-
wild”. This implied that after a RAP presentation,
an additional bar chart, together with a small expla-
nation about filler words, was added to the student’s
RAP report. Figure 4 depicts an example of a bar
chart, presented in a student’s RAP report, with the
most common filler words used by the student during
their presentation. The chart was only informative, no
evaluation of the use of filler words was presented to
the student.
All students were informed during class sessions
about the usage of a new experimental feature and
presented with an online informed consent form ex-
plaining the nature of the experiment and how their
data will be handled. Consent was completely op-
tional with no effect to their academic activities and
could be withdrawn at any time by the students.
Consent implied using the student’s data (academic
metadata and RAP results) for this research in an
anonymized aggregated form. We contrasted the stu-
dents’ RAP results against three variables: gender,
study program, and academic performance. Gender is
self-reported by the student, study program and aca-
demic performance (grade average) are part of the stu-
dent’s academic metadata.
In total, 290 students from three different courses
gave their informed consent (90% of enrolled in these
courses) to participate with their data on this research.
Specifically, 211 from the course Communication, 43
from the course Embedded Systems, and 49 from the
course Computation and Society . Communication
students used the RAP system twice in the semester,
the rest used the system only once. Some Communi-
cation students (13) were also enrolled in Computa-
tion and Society and consequently used the RAP sys-
tem three times. Overall, 501 presentation recordings
were obtained from participants.
Additionally, we performed a post hoc detection
of filled pauses on the 501 recordings using the Whis-
per methodology with the objective of evaluating its
effectiveness on real RAP presentations. These re-
sults were not presented in the student’s RAP report
because they were obtained several weeks after their
To measure the behavior of both STT technologies
(Whisper and Coqui STT), an analysis of 64 au-
dios was carried out, all recorded in the RAP class-
room and each with an average duration of 5 minutes.
These standardized conditions ensured consistency in
data collection and provided a controlled scenario for
our assessments.
Table 1: FWER percentage by STT technology.
STT technology FWER [%]
Whisper 35.85
Coqui STT 90.80
Table 1 summarizes the error rate of filler words
detection. There is a significant disparity in the ac-
curacy of both technologies. According to Errattahi,
the WER is a good metric to know which ASR tech-
nology is better than another (Errattahi et al., 2018);
taking into account that the FWER metric was based
on the WER and that the filler word error rates of
Whisper and Coqui STT technologies are 35.85% and
90.80% respectively, we can state that Whisper is bet-
ter than Coqui STT for detecting filler words in oral
presentations. This may be because most of the dic-
tionary of common filler words were added as hot
words for Coqui STT.
Table 2: FWER percentage by Gender using Whisper.
Gender Num. Audios FWER [%]
Women 21 27.47
Men 43 26.87
Table 2 shows interesting patterns in the detection
of filler words according to the gender of the speaker
using Whisper. The FWER for women’s presenta-
tions is 27.47%, while for men’s presentations it is
26.87%. These results suggest a consistency in the ef-
fectiveness of Whisper in the detection of filler words
regardless of the gender of the speaker and indicates
that there is no gender bias in the detection of filler
words. As there might be gender differences in the
use of speech disfluencies, it is important to rule out
gender bias in the detection technology.
Regarding filled pauses, 55 of these same 64 au-
dios were used. There were 694 real filled pauses. We
listened each detected filled pause clip, for both Whis-
per and Praat implementations, to count true and false
positives and used manual transcripts for the false
Table 3: Performance metrics of filled pause detection.
Implementation F-score FPs Avg.
Whisper 0.8816 1.29
Praat 0.3541 17.09
Detecting Speech Disfluencies Using Open-Source Tools in Automatic Feedback Systems for Oral Presentation Training
Figure 3: Phases of audio evaluation to obtain both filler words and filled pauses.
Figure 4: Example of a report of frequency of usage of filler
words in a five-minute presentation.
Table 3 presents the obtained F-scores and false
positives averages for the two implementations of the
filled pause detection algorithm.
Table 4: Expanded performance metrics of filled pause de-
Impl. TP FN FP Accuracy Recall
Whisper 603 91 71 0.9120 0.8689
Praat 316 775 378 0.3247 0.4553
Table 4 collects the individual calculated metrics.
The accuracy value is the weighted mean of the accu-
racy of each recording analysis.
4.1 On-the-Wild Evaluation
Some recordings, due to unexpected background
noises and technical errors, were not processed (488
out 501 were processed for filler words, 481 out of
501 were processed for filled pauses). Use of the
RAP system was divided in two sessions during the
semester, Table 5 explains the use of the RAP system
by subject.
Table 5: RAP sessions by subject used in the evaluation.
Subject Session 1 Session 2
Communication YES YES
Embedded Systems YES NO
Computation and Society NO YES
Figure 5 shows the filler words results by gender
(women = 109, men = 177) and Figure 6 by study
program. It can clearly be seen that, on average as
reported by the mean and the median, men (mean
= 12.9) use more filler words than women (mean
= 8.9) in a five-minute oral presentation. As data
is non-normally distributed, a Wilcoxon non-paired
two-sample t-test was performed on results on the first
and second sessions. On both cases, highly statis-
tical differences were observed. As for study pro-
grams, statistically significant differences (using the
same test) were observed between engineering pro-
grams and non-engineering programs. For exam-
ple, on average, Electronics and Computing engineer-
ing students reported higher (mean = 13.8) use of
filler words than Design and Communication students
(mean = 8.6). Statistically significant differences re-
mained, to a lesser degree due to diminished sample
size, when controlling by gender and voice volume.
No statistically significant differences were observed
by academic performance. No statistically significant
differences were observed for Whisper filled pauses
on any variable.
Figure 7 shows the Pearson correlation statistics
between RAP results and Whisper filled pauses and
filler words. While all correlations were either non-
existent or weak, a statistically significant positive
correlation (r ̸= 0) was observed between filler words
and filled pauses (as extracted by Praat) and a nega-
tive correlation between filler words and gaze. As ex-
CSEDU 2024 - 16th International Conference on Computer Supported Education
Figure 5: Use of filler words by gender in RAP presenta-
tions in the first and second sessions.
Figure 6: Use of filler words by study program in RAP pre-
sentations, both sessions.
pected, a positive, but rather weak, correlation is ob-
served between both filled pauses (Whisper and Praat)
detection algorithms.
Figure 7: Correlation between RAP scores and Whisper ex-
tracted filler words and filled pauses.
Whisper, with its medium model and ability to tran-
scribe audios in Spanish with 3.6% WER, offers an
opportunity to apply it to the identification of filler
words in oral presentations. The evaluation of Whis-
per technology in the context of the RAP system
shows a FRER error rate of 35.85%, indicating a mod-
erate identification of filler words that can improve
feedback to students in an academic context. This ef-
ficacy is especially significant considering the com-
plexity of identifying filler words in oral presenta-
The results of the on-the-wild evaluation corrob-
orate a pattern that has been observed before in lan-
guage research: there is a gender disparity in the use
of non-clinical speech disfluencies (Bortfeld et al.,
2001; Lo, 2020). On average, men tend to use 40%
more filler words than women in a five-minute RAP
presentation. It is important to point out that the
recordings of male presenters tend to exhibit slightly
higher voice volume levels, which may facilitate the
detection of disfluencies. However, after controlling
for voice volume, the gender effect remains. After
controlling for study program, there are more men in
engineering programs, the gender effect still remains,
albeit with a weaker statistical significance due to re-
duced sample size.
As for the poor correlation between both the Praat
and Whisper filled pauses detection algorithms, it
might happen that the latter needs further improve-
ment or that the large number of false positives in the
former interferes. Nevertheless, Whisper results are
quite decent, clearly outperforming the existing Praat
algorithm as it is shown in the results tables. Whis-
per small model should certainly be the first choice
for detecting filled pauses, before trying to fine-tune a
model or creating a new detector from scratch.
In previous work, we found that engineering stu-
dents tend to do poorly with non-verbal oral presen-
tation skills, as measured by posture and gaze to the
audience (Dom
ınguez et al., 2023). It is interesting to
note that this pattern is also observed in verbal skills
as measured by filler words. Also, the inverse correla-
tion (r = -0.29, highly significant), observed between
gaze to the audience and filler words suggest a pos-
sible correlation between verbal and non-verbal oral
presentation skills.
This study presents the evaluation of different tools to
detect both filler words and filled pauses. For filler
Detecting Speech Disfluencies Using Open-Source Tools in Automatic Feedback Systems for Oral Presentation Training
words, Whisper outperforms Coqui STT, while pro-
viding similar performance across gender disparities.
For filled pauses, the small Whisper model provides a
balance between good accuracy and recall, compared
to the medium model.
While the Whisper algorithms have room for im-
provement, the tool performs well on-the-wild and
may allow the exploration of possible correlations be-
tween verbal and non-verbal oral presentation skills.
We express our sincere gratitude to Maria Gonzalez,
Nicole Asqui and Hayleen Carrillo for their invalu-
able collaboration in the execution of this project. We
also extend our appreciation to all the contributors to
the open source libraries and tools used in its devel-
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