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# Assessing Cancer-Related Cognitive Impairment for breast cancer survivors with speech analysis

Amélie B. Richard, Manon Lelandais, Sophie Jacquin-Courtois

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Amélie B. Richard, Manon Lelandais, Sophie Jacquin-Courtois. Assessing Cancer-Related Cognitive Impairment for breast cancer survivors with speech analysis. Speech Communication, 2025, 174, pp.103284. 10.1016/j.specom.2025.103284. hal-05199420

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# Assessing Cancer-Related Cognitive Impairment for breast cancer survivors with speech analysis

Amélie B. Richard<sup>a,b,*</sup>, Manon Lelandais<sup>c,‡</sup>, Sophie Jacquin-Courtois<sup>b,d</sup>

## ARTICLE INFO

### Keywords:

Cognitive impairment

Cancer

Speech analysis

Prosody

Pauses

## ABSTRACT

**Background:** This paper presents a first study on the assessment of Cancer-Related Cognitive Impairment (CRCI) with speech analysis. CRCI is a reported change in cognitive performance by cancer patients, which is too subtle to be detected by the current tests in use. Speech analysis might provide a solution to this issue. Speech analysis can be instrumental in the detection of subtle cognitive impairment, as speech contains fine-grained segmental and suprasegmental parameters that are sensitive to changes in cognition. Pauses have particularly been highlighted as potential behavioral markers of neurocognitive disorders. However, the absence of simple, detailed methods compromises the feasibility of speech analyses in clinical practice.

**Objectives:** This study aims (i) to identify breast cancer survivors with CRCI using a new practical method centered on speech pauses, and (ii) to provide the detailed protocol that supports this study, which is intended to be applicable in clinical contexts.

**Methods:** Thirty-three breast cancer survivors with a cognitive complaint, eleven breast cancer survivors without a cognitive complaint and thirteen controls were included in the study. Participants were instructed to tell a picture-based story. Their narratives were recorded, automatically transcribed with Whisper, and analyzed using the SPPAS and Praat software. Silent pauses, filled pauses, and sustained vowels were annotated, then processed in RStudio for statistical analysis.

**Results:** Only silent pause duration was significantly longer for breast cancer survivors with a cognitive complaint than for controls.

**Conclusions:** The results suggest that silent pause duration is a good marker for detecting CRCI. Automatizing the transcription and annotation of speech data improves the feasibility of speech analysis in clinical contexts, although a manual check is required.

## 1. Introduction

This paper describes a pilot study in which we use speech analysis to discriminate between patients with Cancer-Related Cognitive Impairment (henceforward CRCI) who have complained about cognitive change and healthy controls without any complaint. In the following subsections, we detail how speech analysis has been shown useful in the detection of cognitive impairment and why semi-automatic methods that are easy to implement are still needed in clinical contexts. We then describe the specific category of cognitive impairment our case population suffers from, along with the objectives and hypotheses of our pilot study.

Our pilot study is supported by a protocol for the analysis of prosody for patients with subtle cognitive impairment. The method it follows is intended to facilitate the diagnosis of subtle cognitive impairment. It is primarily addressed to certified speech and language therapists, students in speech and language pathology, and researchers who are interested in fluency-related phenomena.

### 1.1. Cancer-Related cognitive impairment

Cancer-Related Cognitive Impairment is a form of subtle cognitive impairment induced by cancer (except central nervous system cancer) and/or its treatments, which may affect up to 75 % of oncology patients

<footnote><sup>a</sup> Université de Montpellier Paul-Valéry, CNRS UMR5267; Praxiling, Montpellier 34090, France</footnote>

<footnote><sup>b</sup> Université de Lyon, Université Lyon 1, INSERM U1028; CNRS UMR5292, Lyon Neuroscience Research Centre, Trajectoires Team, Lyon F-69676, France</footnote>

<footnote><sup>c</sup> Université Paris Cité, ALTAE UR3967, Paris F-75013, France</footnote>

<footnote><sup>d</sup> Physical Medicine and Rehabilitation Department, Henry-Gabrielle Hospital, Hospices Civils de Lyon, Pierre-Bénite 69610, France</footnote>

<footnote>* Corresponding author at: Praxiling, Route de Mende, 34090, Montpellier, France.
E-mail address: amelie.richard@univ-montp3.fr (A.B. Richard).</footnote>
(Lange et al., 2019). CRCI alters patients' quality of life (Von Ah et al., 2022). For some, the symptomatology is long-lasting, lasting more than two years after oncology treatments have been stopped (Whittaker et al., 2022). The persistence of this disorder limits participation in social activities and hinders the return to work for a potentially young population (Dumas et al., 2020; Haywood et al., 2023). The subtle nature of CRCI makes it an invisible disability that is often under-diagnosed, even though appropriate assessment and treatment options are available.

CRCI is characterized by a cognitive complaint affecting several aspects of cognition (memory, executive functions, attention, processing speed, language) that can be objectified by standardized questionnaires (e.g., FACT-Cog, Wagner et al. [2009]). One of the most frequent complaints is forgetting words. Despite the intensity of the complaint, the current speech and neuropsychological tests in use struggle to fully display the difficulties reported by patients (Costa and Fardell, 2019). Scores obtained on verbal fluency and naming tests in particular are often within the norm. It is therefore necessary to develop new approaches and/or new tools to detect such subtle cognitive disorders.

## 1.2. Linguistic markers of cognitive impairment

The literature in aphasiology, whether vascular or neurodegenerative, has shown the benefit of analyzing patients' speech and voice to reveal cognitive disorders (see for instance Martínez-Nicolás et al., 2021). Speech analysis has been found useful to assess the cognition of patients because of its non-invasive nature, its sensitivity to subtle cognitive changes and its high ecological validity (Lanzi et al., 2023). Speech can be studied through segmental and suprasegmental analyses, depending on whether the focus is on singular segments such as phonemes, or on attributes that can extend over phonemes such as fundamental frequency (Hz) and tones, intensity (dB) or rhythm, e.g., with duration (ms). Prosody, i.e., the study of speech elements including suprasegmental phenomena, is particularly relevant to investigate mild cognitive impairment, as it contains fine-grained elements that can signal changes in a speaker's cognition (Ivanova et al., 2023). These prosodic elements include parameters related to voice quality, such as variations in intonational amplitude (Themistocleous et al., 2020), as well as broader speech units such as pauses (Vincze et al., 2021). For instance, Parjane et al. (2021) compared descriptive speech productions between patients with the nonfluent-agrammatic variant of Primary Progressive Aphasia, patients with Corticobasal Degeneration and healthy controls. Both patient groups showed longer, more frequent pauses and reduced fundamental frequency ranges (i.e., F0) compared to controls.

However, the variability of the methodologies in use (e.g., elicited speech type, linguistic variables, tools for analysis) makes it difficult to compare results across studies even for studies with similar objectives (Richard et al., 2024b). This is even more the case for cancer patients whose cognition has not been investigated through speech yet. As part of a harmonization effort, Angelopoulou et al. (2024) proposed a speech analysis method that was used to compare two types of elicited descriptive speech (i.e., a picture description and an autobiographical description) in a healthy population. The interest of their study mainly lies in the description of the measurement of nineteen speech variables, although some concepts such as "words" or "pauses" are not defined (e.g., the authors do not differentiate pauses due to physiological constraints from pauses having a demarcative or hesitation function).

However, no mention of semi-automatic tool is made in the methodology, which limits its feasibility in clinical practice. The authors for instance report an orthographic transcription stage during which they applied coding conventions, but they did not specify whether they used an automatic transcription tool or script. The transcription and annotation stages are time-consuming, and are therefore difficult to apply and replicate in clinical practice. A detailed method that is easy to implement in the clinic is still needed.

## 1.3. Semi-automatic speech analysis

In this section, we argue for the use of automatic, off-line and open source software.

### 1.3.1. Use of automatic tools

Speech analysis is a long and tedious task. After recording a patient's oral production, it is often necessary to transcribe it, either orthographically or phonetically. Manual transcription is especially time-consuming. Transcribing one minute of speech may take up to twelve minutes (Bryant et al., 2017). This step is also particularly costly in terms of concentration, since it requires several listening sessions in order to avoid omitting words or disfluencies produced by the speaker. For instance, determining the exact number of repetitions of a lexical or phonemic item will typically require several takes. Meanwhile, the process of multiple listenings entails biases linked to auditory fatigue, subjectivity and habituation, and thus increases the risk of transcription errors (e.g., addition of words by priming effect).

The use of Artificial Intelligence tools helps to reduce time and such biases by, for instance, providing a "neutral" first transcription. Artificial Intelligence is a broad field in computer science that spans automated cognitive tasks using simple algorithms, understandable machine learning classification, prediction and clustering models, as well as complex deep learning systems such as generative AI. These tools are based on technologies of varying complexity, from simple automated tasks (e.g., automatic detection of silent pauses) to neural network models (e.g., automatic transcription), and offer considerable time-saving advantages for speech analysis (see O'Shaughnessy [2024] for a review of Automatic Speech Recognition systems). However, AI tools can vary in performance depending on the audio quality of the recording. Manual verification is therefore essential to correct any errors made by these tools.

### 1.3.2. Use of offline tools

The recorded voice of patients and study participants represents identifying data that must be protected at a high level of security and confidentiality (see the French Commission Nationale de l'Informatique et des Libertés, 2016). As a result, it is crucial to choose transparent data management tools. In addition, available resources on the internet sometimes have obscure privacy policies, which increases the risk of data leaks. The offline tools described in this study meet the security criteria required for speech analysis in the sense that data is processed from its storage place.

### 1.3.3. Use of open source tools

Open source software is free software whose programming codes are accessible to any user. This type of collaborative tool not only guarantees total transparency in data management, but also reinforces the scientific reproducibility and reliability of results.

## 2. Objective and hypotheses

Pilot studies have shown the benefit of analyzing disfluencies (Richard et al., 2022) particularly pauses, in the speech production of breast cancer survivors with cognitive complaints (Richard et al., 2023). A qualitative analysis of hesitation pauses produced during a picture-based narration task showed potential to differentiate breast cancer survivors with a cognitive complaint from breast cancer survivors without a cognitive complaint and control participants (Richard et al., 2024a).

The aim of the present study is to test whether three different types of pauses can distinguish cancer survivors with a cognitive complaint from those without and healthy controls. We apply a semi-automatized speech analysis method to this population, and provide the full protocol for both research and clinical uses.

Based on these previous studies, we hypothesize that silent pauses,
filled pauses and sustained vowels will be longer (in ms) for breast cancer patients with CRCI (i.e., the presence of a significant complaint according to the FACT-Cog questionnaire) than for breast cancer patients without CRCI and healthy controls.

# 3. Methods

## 3.1. Population

Fifty-seven breast cancer survivors and fourteen age- gender- and education-matched healthy controls volunteered for the current study.

### 3.1.1. Inclusion criteria

Inclusion criteria for all participants were (i) to be a woman aged between 30 and 70, (ii) to be a native French speaker or be fluent in French (iii) to have normal or corrected sight and hearing. In addition, breast cancer survivors had (iv) to have completed curative treatments (i.e., surgery, chemotherapy, radiotherapy) more than four weeks and less than one year before, excluding hormone therapy.

### 3.1.2. Exclusion criteria

Non-inclusion criteria for all participants were (i) a history of oral language disorders, (ii) a neurological or psychiatric history, (iii) the use of medication that is likely to cause cognitive changes. Additionally, participants who had breast cancer had (iv) to not present metastases. Ten breast cancer survivors were not eligible due to brain or tumoral comorbidities, and one healthy control was not eligible for dyslexia.

### 3.1.3. Groups

The FACT-Cog questionnaire is a standardized questionnaire for assessing cognitive complaints in oncology patients (Wagner et al., 2009). A threshold of less than or equal to 55/72 on the Perceived Cognitive Impairment (PCI) subscale indicates the presence of significant cognitive complaints (Van Dyk et al., 2020). Participants completed the French version of the FACT-Cog v3 questionnaire (Costa et al., 2018; Joly et al., 2012). We used this score to split our population into three groups: breast cancer participants with a cognitive complaint (BC<sub>yes</sub>) according to the PCI score of the FACT-Cog questionnaire, breast cancer participants with no cognitive complaint (BC<sub>no</sub>) according to the PCI score of the FACT-Cog questionnaire, and healthy controls (HC).

In total, thirty-three BC<sub>yes</sub> participants, eleven BC<sub>no</sub> participants, and thirteen age- and education-matched HC participants were eligible.

## 3.2. Narrative task

The given task to the participants was a narrative speech task based on a sequence of five images, in which participants had to imagine the sixth (Bézy et al., 2016). The images tell the story of a family arriving at the beach. The father is windsurfing while his son and wife are on the shore. Suddenly the wind picks up and the father falls into the water. The last image shows the alarmed mother and son, with the mother calling for help on the phone. Participants were instructed to tell the story based on the images they see, with no time limit.

## 3.3. Recording material and conditions

The narrative productions of 35 participants were recorded in a quiet room at the Henry Gabrielle rehabilitation hospital (Saint-Genis-Laval, France) using a Rode Lavalier Go® omnidirectional lavalier microphone connected to a laptop.

The speech samples from the 22 remaining participants were recorded in an anechoic booth at the Lyon Neuroscience Research Centre (Bron, France) using a Shure® omnidirectional lapel microphone lined to an M-Audio® portable audio interface.

## 3.4. Linguistic parameters

Based on our previous pilot studies, we analyzed silent pauses, filled pauses and sustained vowels (also referred to as lengthenings in the literature). Silent pauses (SP) were segments characterized by an absence of speech, cough, or yawn during 200 ms or more, from the first to the last speech phonemes. Silent pauses might include lip or tongue noise or breathing. Filled pauses (FP) were non-semantic voiced segments such as “uh” and “uhm” produced for 200 ms or more with a flat F0 contour. Sustained vowels (SV) were prolonged vowels produced for 200 ms with a flat F0 contour. We extracted mean durations for silent pauses, filled pauses and sustained vowels for each participant.

## 3.5. Prosodic analysis

The following section describes the different steps in the prosodic analysis, starting from the acoustic signal. Depending on the reader's needs, sections can be used independently from each other. A full tutorial of our method is available (see the Supplementary material section).

### 3.5.1. Acoustic signal pre-processing

Before starting our prosodic analyses, we segmented the audio recording to only retain the narrative production. This step was carried out using the free Audacity software (Audacity Team, 1999).

### 3.5.2. Transcription

We used automatic speech transcription with the large-v3 Whisper model (Radford et al., 2022). Whisper is an open source code dedicated to automatic speech recognition. As part of its functionalities, Whisper provides an orthographic transcription of speech from the acoustic signal. A detailed description of how to install and use Whisper is available (see the Supplementary material section).

We checked each text for transcription errors, and manually added silent pauses (#) and filled pauses (uh). It is important to note that Whisper removes all disfluencies (e.g., pauses, repetition, false starts, etc.) by default.

Finally, we cleaned up the data, for instance removing punctuation marks or replacing numbers with their equivalent in letters. The Python code developed for this study is available (see the Supplementary material section).

### 3.5.3. Pause annotation

We annotated pauses with the SPPAS software (Bigi, 2015). SPPAS is an open source software package that has specifically been developed for speech analysis. It provides a wide range of automatic analyses, such as silent pause detection, grapheme-phoneme conversion and syllabification of transcriptions aligned with the acoustic signal (i.e., sound). Its graphical interface and interoperability (i.e., its ability to work with other software in terms of compatibility formats) make it equally suitable to research and clinical applications. To refine our analyses, we also used the free Praat software (Boersma and Weenink, 2024). This software has been designed for acoustic signal analysis and visualization, as well as the extraction of numerous prosodic variables (e.g., fundamental frequency).

The following paragraphs describe the steps we took for the data annotation phase.

We first uploaded the (.wav format) recordings and (.txt format) transcriptions into SPPAS. From the "Annotation" tab, we searched for Inter-Pausal Units (i.e., IPUs), then ran the automatic transcription fill-in. The first function, "Search for IPUs", automatically detects silent pauses, while the second, "Fill-In IPUs", fills in empty spaces with the transcription of speech segments. These functions can be accessed via the "STANDALONE" button. We have chosen the ".textgrid" output format, which is processed by Praat.

We then performed a verification step by opening the .textgrid files
in Praat. We readjusted the boundaries of silent pauses when they didn't match the acoustic signal. This represented no readjustment in good quality recordings, and approximatively 1–5 readjustments in poor quality recordings of ~82 s.

In the SPPAS "Annotation" tab, we ran the "Text Normalization", "Phonetization" and "Alignment" functions simultaneously. This step segments the audio signal into phonemes from the text, and temporally aligns the phonemes with the acoustic signal. Vowel lengthenings and filled pauses can this way easily be identified based on their duration of 200 ms or more.

We ran another check in Praat, opening the .textgrid files, now named -palign, to ensure that the phonemes were correctly aligned with the acoustic signal.

In the SPPAS "Analyze" tab, we opened the -palign .textgrid files, and selected the "TokenAlign" annotation tier. We applied a filter (i.e., single filter) to create a new tier named "pauses". This tier contains vowel phonemes and pauses (i.e., U~, /A/, /O/, @, i, E, e, j, H, a~, O~, y, 2, 9, u, fp, # for French according to SPPAS conventions) of 200 ms or more. In Praat, we only retained vowel lengthening annotations that had a flat intonation curve.

Finally, we harmonized all annotations by replacing the existing annotations in the "pauses" tier with the following labels: SP for Silent Pauses, FP for Filled Pauses and SV for Sustained Vowels. To this effect we ran the text replacement script developed by C. DiCanio (2011) in Praat, available at the following link: https://www.acsu.buffalo.edu/~cdicanio/scripts.html.

### 3.5.4. Inter-annotator agreement

Calculating inter-annotator agreement consists in giving a share of the data (usually about 20 %) along with its annotation protocol (i.e., a description of the elements to annotate) to one or more individuals who are naïve to the dataset and the study goals to ensure the validity and reliability of the annotation process. It is measured with Cohen's κ, which score ranges from 0 to 1. A score equal to or above 0.81 confirms the reliability of the annotation.

We gave 20 % of our dataset to two naïve annotators to verify that our definition of silent pauses was replicable. The inter-annotator agreement reached 98.71 % of agreement, qualifying as near perfect (κ = 0.97). For filled pauses, agreement was 90.98% (κ = .81, near perfect). For sustained vowels, agreement was 86.88% (κ = .74, substantial).

### 3.5.5. Extraction of prosodic data

We extracted mean pause duration as well as standard deviation for all three pause types in each speech sample with SPPAS. In the "Analyze" tab, we selected all "pauses" tiers and opened the "Statistics" option to access data spreadsheets.

## 3.6. Psychological and cognitive assessment

In addition to the FACT-Cog questionnaire as a subjective assessment of CRCI, participants underwent cognitive screening and psychological evaluations to control for confounding variables that might influence pause production.

Participants took the Montreal Cognitive Assessment test (Nasreddine et al., 2005). This rapid, self-administered test is designed to detect fine-grained cognitive impairment by assessing several cognitive domains (e.g., memory, visuo-spatial abilities, language). A score below 26/30 indicates the presence of cognitive decline.

Participants completed the Hospital Anxiety Depression (HAD) scale (Zigmond and Snaith, 1983). This questionnaire comprises two subscales, designed to assess anxiety (HAD-A) and depression (HAD-D). A score above 11/21 on either subscale indicates the presence of anxiety or depression.

Finally, participants completed the Functional Assessment of Chronic Illness Therapy - Fatigue (FACIT-F) (Bonomi et al., 1996; Cella

et al., 1993). This questionnaire was specially designed to assess overall cancer-related fatigue. The higher the score, the greater the fatigue. There is no standardized threshold for this questionnaire.

## 3.7. Statistical analyses

The statistical analyses in this study were run using R language (R Core Team, 2021) in RStudio (RStudio, BPC, 2020). This software is free, off-line and open source. With multiple online tutorials and its active community, R is easy to use for parametric and non-parametric frequentist and Bayesian statistical models, as well as data visualization. Our data is stored in a spreadsheet in .csv format. Each column corresponds to a variable, and each row to a participant. Fig. 1 shows the ordering of the data after its importation in RStudio.

Before starting the analyses, we checked the assumption of normality of our data by running a Shapiro-Wilk test (Shapiro and Wilk, 1965). The result gave a significant *p*-value, meaning that our dataset was not normally distributed. We consequently chose non-parametric tests for our statistical analyses. We ran Kruskal-Wallis U tests (Kruskal and Wallis, 1952) to compare the variables of the BC<sub>yes</sub> participants with those of the BC<sub>no</sub> and the HC participants, and Dunn's post-hoc analyses for multiple comparisons (Dunn, 1964) to see which group differed from the others. Finally, we performed a Spearman correlation (Spearman, 1904) to test the possibility of an effect of demographic and psychological variables and (i.e., age, anxiety, depression, fatigue) on pauses.

# 4. Results

## 4.1. Demographic and psychological data

Fifty-seven participants took part in this study (BC<sub>yes</sub> N = 33, BC<sub>no</sub> N = 11, HC N = 13). There was no missing data except for one participant who did not fill in the questionnaires on fatigue (FACIT-F), anxiety (HAD-A), and depression (HAD-D). Anxiety and depression scores were below alert thresholds for all groups.

Table 1 summarizes the results of the Kruskal-Wallis test comparing the demographic and psychological variables between groups.

### 4.1.1. Age

A Kruskal-Wallis test indicated there was no significant age difference across the three groups ($\chi^2$ (2, N = 57) = 4.85, *p* = .088).

### 4.1.2. Screening of cognition

A Kruskal-Wallis test indicated there was no significant difference in the MoCA cognitive screening across the three groups ($\chi^2$ (2, N = 57) = 2.668, *p* = .263). This result means that no participant showed cognitive decline according to the screening test.

### 4.1.3. Fatigue

A Kruskal-Wallis test indicated a significant difference in reported fatigue as measured with the FACIT-F questionnaire across the three groups ($\chi^2$ (2, N = 56) = 26.422, *p* < .001). Post-hoc comparisons using Dunn's test showed that the median fatigue score for BC<sub>yes</sub> was significantly higher than that for BC<sub>no</sub> and HC, *p* < .001. However, no significant difference was found in the median fatigue scores for BC<sub>no</sub> and HC, *p* = .174. One participant (BC<sub>yes</sub>) did not fill in the questionnaire.

### 4.1.4. Anxiety

A Kruskal-Wallis test showed a significant difference in reported anxiety as measured with the anxiety subscale of the HAD scale across the three groups ($\chi^2$ (2, N = 56) = 7.710, *p* = .021). Post-hoc comparisons using Dunn's test showed significantly higher median anxiety scores for BC<sub>yes</sub> than for HC, *p* = .029. However, no significant difference was found in median anxiety scores between BC<sub>yes</sub> and BC<sub>no</sub>, *p* = .195, and between BC<sub>no</sub> and HC *p* = .557. One participant (BC<sub>yes</sub>) did not fill in the questionnaire. Anxiety scores remained below alert thresholds for all
<table><caption>Fig. 1. Data ordering from four participants in RStudio.</caption><thead><tr><th>ID</th><th>Age</th><th>Group</th><th>FACT_Cog_PCI</th><th>MoCA</th><th>SDMT</th><th>FACIT</th><th>HAD_A</th><th>HAD_D</th><th>SP_mean</th></tr><tr><th>&lt;chr&gt;</th><th>&lt;int&gt;</th><th>&lt;chr&gt;</th><th>&lt;int&gt;</th><th>&lt;int&gt;</th><th>&lt;int&gt;</th><th>&lt;int&gt;</th><th>&lt;int&gt;</th><th>&lt;int&gt;</th><th>&lt;dbl&gt;</th></tr></thead><tbody><tr><td>AL01</td><td>41</td><td>BC</td><td>60</td><td>29</td><td>62</td><td>46</td><td>8</td><td>1</td><td>0.7843750</td></tr><tr><td>AL02</td><td>39</td><td>BC</td><td>72</td><td>30</td><td>71</td><td>39</td><td>5</td><td>2</td><td>0.6536719</td></tr><tr><td>AL03</td><td>68</td><td>BC_cog</td><td>31</td><td>27</td><td>42</td><td>19</td><td>10</td><td>9</td><td>1.2844118</td></tr><tr><td>AL04</td><td>41</td><td>BC_cog</td><td>38</td><td>26</td><td>56</td><td>38</td><td>0</td><td>2</td><td>1.2593611</td></tr></tbody></table>

<table><caption>**Table 1**

Characteristics of the population. Median, *SD*.</caption><thead><tr><th>Variables</th><th>BC<sub>yes</sub></th><th>BC<sub>no</sub></th><th>HC</th><th>p-value</th></tr></thead><tbody><tr><td>N =</td><td>33</td><td>11</td><td>13</td><td></td></tr><tr><td>Age</td><td>50 (8.7)</td><td>46 (8.4)</td><td>42 (10.2)</td><td>.088</td></tr><tr><td>FACT-Cog PCI (/72)</td><td>35 (12.2)<sup>*†</sup></td><td>62 (5.2)<sup>†</sup></td><td>68 (4.6)<sup>**</sup></td><td>&lt; 0.001</td></tr><tr><td>MoCA (/30)</td><td>27 (2.2)</td><td>29 (1.8)</td><td>28 (2.3)</td><td>.263</td></tr><tr><td>FACIT-F (/52)</td><td>28 (13.2)<sup>*†</sup></td><td>44 (13.8)<sup>†</sup></td><td>47 (6.9)<sup>**</sup></td><td>&lt; 0.001</td></tr><tr><td>HAD-A (/21)</td><td>10 (4.4)<sup>**</sup></td><td>7 (2.8)</td><td>5 (4.0)<sup>**</sup></td><td>.021</td></tr><tr><td>HAD-D (/21)</td><td>7 (3.7)<sup>**</sup></td><td>4 (2.9)</td><td>2 (1.6)<sup>**</sup></td><td>&lt; 0.001</td></tr></tbody></table>

BC<sub>yes</sub> = breast cancer participants with a significant cognitive complaint, BC<sub>no</sub> = breast cancer participants without any significant cognitive complaint, HC = healthy controls.

* = significant difference between BC<sub>yes</sub> and HC.

† = significant difference between BC<sub>yes</sub> and BC<sub>no</sub>.

groups.

### 4.1.5. Depression

A Kruskal-Wallis test indicated a significant difference in reported depression as measured with the HAD depression subscale across the three groups ($\chi^2(2, N = 56) = 20.841, p < .001$). Post-hoc comparisons using Dunn's test showed a higher median depression score for BC<sub>yes</sub> than for HC, $p < .001$. However, there was no significant difference in median depression scores between BC<sub>yes</sub> and BC<sub>no</sub>, $p = .063$, and between BC<sub>no</sub> and HC $p = .105$. One participant (BC<sub>yes</sub>) did not fill in the questionnaire. Depression scores remained below alert thresholds for all groups.

## 4.2. Corpus characteristics

Our corpus is made of 57 speech samples, with a 79-minute total duration (mean per sample = 82 s ± 32 s). A total of 2823 pauses were annotated, with 634 silent pauses, 1229 filled pauses, and 960 sustained vowels.

## 4.3. Pause analysis results

Given our small sample size and our objective to stay close to participants' production, we decided to keep outliers in.

### 4.3.1. Silent pauses

Fig. 2 shows the distribution of mean silent pause duration overlapped across the three groups. While BC<sub>yes</sub> and HC have distinct curves, the BC<sub>no</sub> distribution is in-between these two groups, with a closer mean to that of BC<sub>yes</sub>. The three curves show a large first peak, followed by a smaller second peak standing for longer pause duration. These two distinct peaks in all three distributions show that in each group, a majority of participants produced a similar mean silent pause duration, while a smaller set of participants produced a longer mean duration than the majority of the group, around 0.400 ms after the group mean. The range of each group is thus composed of two distribution peaks.

Fig. 3 summarizes the distribution of mean silent pause duration. The box plots show the 1st and 3rd interquartiles, as well as median pause duration. Red diamonds display the mean silent pause duration for each group. Each point represents the mean silent pause duration of a participant. As a group, HC shows little variability in results (apart from two points in the higher values), while BC<sub>yes</sub> and BC<sub>no</sub> show larger spreads.

A Kruskal-Wallis test showed a significant difference in silent pauses across the three groups ($\chi^2(2, N = 57) = 15.894, p < .001$). Post-hoc comparisons using Dunn's test indicated a higher silent pause mean duration for the BC<sub>yes</sub> group compared to the HC group, $p < .001$. However, there was no significant difference in silent pause duration between the BC<sub>yes</sub> and BC<sub>no</sub> groups, $p = .143$, and between the BC<sub>no</sub> and HC groups, $p = .108$. Fig. 4 shows the Dunn's test results with BC<sub>yes</sub> as the reference data, with a significant difference between HC vs. BC<sub>yes</sub>, and a non-significant difference between BC<sub>no</sub> and BC<sub>yes</sub>.

The effect size for unbalanced data between the three groups was measured by Cohen's *f* and showed a large effect ($f = 3.2$). However, this result does not explain the overlap between the BC<sub>yes</sub> and BC<sub>no</sub> groups. We performed a power analysis to estimate the required number of BC<sub>yes</sub>, BC<sub>no</sub> and HC for balanced data to reach significance for mean silent pause duration results. The power analysis indicates that a sample size of 53 participants will provide enough data to expect a medium effect size with a 95 % Confidence Interval (pwr.anova.test($k = 3, f = 0.4, sig.level=0.25, power=0.8$)). A medium effect size will keep the inherent interspeaker variability of speech production.

### 4.3.2. Filled pauses

A Kruskal-Wallis test showed there was no significant difference in

<figure><img src="image_3.png" /><figcaption>Fig. 2. Overlapping distributions of mean silent pause duration (ms) for all three groups. Dotted lines show the mean silent pause duration for each group.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Fig. 3. Distribution of mean silent pause duration for each group. Red diamond = mean duration per group. Dots = mean duration per participant.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 4. Boxplots showing mean silent pause duration for each group. **** = significant difference between HC and BC<sub>yes</sub>. ns = non significant difference between BC<sub>no</sub> and BC<sub>yes</sub>.</figcaption></figure>

filled pause duration across the three groups ($\chi^2(2, N = 57) = 1.574, p = .455$).

### 4.3.3. Sustained vowels

A Kruskal-Wallis test showed there was no significant difference in sustained vowel duration across the three groups ($\chi^2(2, N = 57) = 4.469, p = .107$).

Table 2 summarizes the Kruskal-Wallis test results, comparing mean pause duration across groups.

<table><caption>**Table 2**
Mean (sd) pause duration per group.</caption><thead><tr><th>Pause types</th><th>BC<sub>yes</sub></th><th>BC<sub>no</sub></th><th>HC</th><th>p-value</th></tr></thead><tbody><tr><td>Silent Pauses</td><td>.897 (0.192)*</td><td>.804 (0.185)</td><td>.615 (0.191)*</td><td>&lt; .001</td></tr><tr><td>Filled Pauses</td><td>.649 (0.197)</td><td>.599 (0.187)</td><td>.520 (0.186)</td><td>.455</td></tr><tr><td>Sustained Vowels</td><td>.415 (0.078)</td><td>.356 (0.067)</td><td>.381 (0.057)</td><td>.107</td></tr></tbody></table>

BC<sub>yes</sub> = breast cancer participants with a significant cognitive complaint, BC<sub>no</sub> = breast cancer participants without any significant cognitive complaint, HC = healthy controls.

### 4.3.4. Co-factor control

Some psychological factors or humor states such as depression are known to influence speech production (Albuquerque et al., 2021). We performed a Spearman's rank correlation to examine the relationship between mean pause duration and reported fatigue, anxiety, and depression scores. Note that none of these scores are considered below pathological threshold. Therefore, they do not qualify as clinical observation. Correlations were only conducted for statistical purposes. Additionally, lower scores on the fatigue scale show more reported fatigue, while higher scores on the anxiety and depression scales signal more reported anxiety and depression.

There was a negative correlation between mean silent pause duration and fatigue scores, r(2) = -0.42, p < .001, and a positive correlation between mean silent pause duration and anxiety scores, r(2) = 0.32, p = .015. In addition, there was a positive correlation between mean silent pause duration and depression scores, r(2) = 0.60, p < .001. However, correlations coefficients were low, suggesting that mean silent pause duration is poorly influenced by co-factors.

Fig. 5 displays the correlation matrix of the relationship between

<figure><img src="image_6.png" /><figcaption>Fig. 5. Correlation matrix of the relationship between mean silent pause duration and co-factors. A red color indicates a negative correlation. A blue color indicates a positive correlation. Numbers = correlation coefficient. Note that the scores remain below alert thresholds.</figcaption></figure>

<footnote>* = significant difference between BC<sub>yes</sub> and HC.</footnote>
mean silent pause duration and the co-factors. Fatigue scores are negatively correlated with anxiety and depression scores, meaning that participants who reported fatigue also reported anxiety, r(2) = -0.65, *p* < .001, and depression, r(2) = -0.72, *p* < .001.

There was no correlation between mean filled pause duration and fatigue scores, r(2) = -0.15, *p* = .256, and between mean filled pause duration and anxiety scores, r(2) = 0.12, *p* = .382. There was no correlation either between mean filled pause duration and depression scores, r(2) = 0.13, *p* = .323.

There was no correlation between mean sustained vowel duration and fatigue scores, r(2) = -0.14, *p* = .290. However, there was a positive correlation between mean sustained vowel duration and anxiety scores, r(2) = 0.02, *p* = .826. There was also a positive correlation between mean sustained vowel duration and depression scores, r(2) = 0.10, *p* = .435.

# 5. Discussion

The goal of our study was to test the potential use of pauses as a behavioral marker of Cancer-Related Cognitive Impairment. We measured silent pause, filled pause, and sustained vowel duration in a dataset of patients with Cancer-Related Cognitive Impairment and healthy controls. As a secondary goal, we aimed to showcase a detailed method using free off-line tools to deal with clinical data, in continuity with Angelopoulou et al. (2024) who provide a protocol for the analysis of connected speech. In the following subsections, we discuss the main result in our dataset analysis, i.e., the role of silent pauses in the detection of Cancer-Related Cognitive Impairment. We then discuss the main advantages of our method and point out a number of starting points for further technical development.

## 5.1. Are silent pauses a speech marker of CRCI?

Based on our team's previous studies, we aimed to investigate silent pauses, filled pauses, and sustained vowels in breast cancer survivors with and without a cognitive complaint (i.e., CRCI) as well as healthy controls. The results show that in all three pause types, only mean silent pause duration can differentiate breast cancer survivors with CRCI from those without CRCI and healthy controls. Interestingly, this finding echoes other studies on pauses in individuals with different etiologies who suffer from subtle cognitive impairment. For instance, Bóna (2024) examined silent pauses, filled pauses and lengthenings among other types of disfluencies in individuals with Multiple Sclerosis. Multiple Sclerosis (MS) is a condition that impacts motor abilities but can also cause cognitive impairment (Patti et al., 2015). In a cohort of twenty MS participants with no or mild dysarthria, Bóna found that only silent pauses were able to differentiate MS patients from healthy controls. This concerned both silent pause duration and frequency, as participants with MS produced more and longer silent pauses than controls.

Despite different etiologies and linguistic systems, silent pauses might better reflect a higher processing load than their voiced counterparts, i.e., filled pauses and sustained vowels. This idea has long been suggested by Ferreira (1993) and is supported by recent evidence in the field. Similarly, Betz et al. (2023) found a higher number of silent pauses compared to filled pauses and lengthenings in a card game experiment on the impact of cognitive load on speech. However, their results showed that lengthenings also increased with task difficulty. The authors related this phenomenon to the co-occurrence of lengthenings and silent pauses in their data, and to the nature of the speech task they used.

Our study presents a limitation regarding the number of participants, as the data is unbalanced. Our results need to be confirmed with further data since our sample size per group is relatively small. The power analysis we ran to estimate the ideal number of participants indicates that forty-eight participants per group are needed to increase the reliability of the results.

## 5.2. Reshaping speech analysis

We transcribed and annotated fifty-seven speech samples, with a total duration of seventy-nine minutes. Automatic Speech Recognition and Natural Language Processing tools considerably reduced the time that was spent on speech transcription and annotation.

### 5.2.1. Speeding up transcription while making it safer

Many private companies offer transcription solutions (e.g., Dragon, Nvivo). Most of them can be accessed either with a fee or for free online, with no guarantee to the use of the data. Such a lack of transparency represents a major ethical concern, especially when handling medical data. We used Whisper as a bypass to these issues. In addition to being time saving, it kept the data on our computer as a command. Whisper is a large language model that can transcribe more than twenty languages from different language families. However, the efficiency of such a model varies considerably across languages. It is measured via Word Error Rate (WER, i.e., the number of words that were poorly transcribed out of the total number of words in the data). Efficiency also depends on model size and audio quality for the recording. For French, the WER ranges from 5.3 to 10.8 for the large-v3 model. Table 3 shows examples of transcription errors from our corpus.

Depending on the WER for a language, transcriptions need more or

<table><caption>**Table 3**
Examples of transcription errors using the Whisper large v3 model.</caption><thead><tr><th>Erroneous transcription</th><th>Correction Translation to English</th><th>Nature of the error</th></tr></thead><tbody><tr><td colspan="3"><strong>Good audio recording quality</strong></td></tr><tr><td>[les vagues] qui onduent</td><td>[les vagues] qui ondulent</td><td>Phonology: the last phoneme is missing /ðdy/ and /ðdyl/</td></tr><tr><td>son scooter des mères</td><td>[the waves] ripple son scooter des mers his water scooter</td><td>Semantics: homophones "mère" (mother) and "mer" (sea)</td></tr><tr><td>il prend quelque chose dans la glacière</td><td>il prend quelque chose dans la glacière</td><td>Semantics: homophones "glaciaire" (glacial) and "glacière" (cooler)</td></tr><tr><td>un monsieur où papa</td><td>un monsieur ou papa a man or a dad</td><td>Syntax: homophones où (where) and ou (or)</td></tr><tr><td>la planche à voile du papa qui du coup est tombée dans l'eau</td><td>la planche à voile du papa qui du coup est tombé dans l'eau the dad's windsurf who has fallen off</td><td>Morphosyntax: the image depicts a man drowning (the father has fallen). Whisper makes the verb "tomber" (to fall) agree with the feminine noun "planche à voile" (windsurf) instead of the masculine noun "papa" (dad) because of the ambiguity of the pronoun "qui" (translated in this case "who", but also "which" in other contexts)</td></tr><tr><td colspan="3"><strong>Poor audio recording quality</strong></td></tr><tr><td>une planche de soeur</td><td>une planche de surf a surf board</td><td>Phonology: the last phoneme is missing, /sær/ (sister) and /sørf/ (surf)</td></tr><tr><td>pas loin des cocotis</td><td>pas loin des cocotiers not far from the coconut tree</td><td>Phonology: the last phoneme is missing /kokoti/ and /kokotje/</td></tr><tr><td>sans que ni la maman ni le petit garçon ne remarque</td><td>sans que ni la maman ni le petit garçon ne remarquent without either the mother or the little boy noticing.</td><td>Morphosyntax: absence of the plural mark to the verb "remarquer" (to notice) because Whisper does not take into account the link between "maman" (mom) and "garçon" (kid) represented by "ni ... ni" (either)</td></tr></tbody></table>

Good audio recording quality: speech samples recorded in a soundproof booth. Poor audio recording quality: speech samples recorded in a quiet room at the hospital, with echoes, background noise, mumblings from the corridors, and occasional outside noise (e.g., cars and helicopter).
less inspection to fix potential errors. Larger models reduce WER but demand more computer power (CPU/GPU), and increase transcription time. In any case, we strongly recommend manually checking the transcripts to avoid errors. Model accuracy can be improved with Whisper's open source license. However, Whisper uses deep learning technology that is hard to fine-tune. In addition, Whisper automatically removes pauses and disfluencies, and is not yet able to recognize multiple speakers. There is thus a need for easy-to-use scripts that include pause and disfluency detection as well as multiple speaker recognition.

### 5.2.2. Improving precision in prosody analysis

Annotating speech is a repetitive task that requires high attentional resources. We used SPPAS to annotate the fifty-seven samples of our dataset in a few minutes. SPPAS is convenient in that it performs automatic annotations, allows the user to edit these annotations, and provides basic statistics in a single software. SPPAS handles French and English by default, and other languages can be implemented. Just like Whisper, the accuracy of the automatic annotation provided by SPPAS largely depends on audio quality. In the case of our dataset, this translated into segmentation errors during the silence detection and/or phonetization phases. We had a small number of misplaced boundaries for good-quality recordings, but this number increased with poor audio quality. We manually corrected the errors by aligning the boundaries to the audio signal. However, SPPAS lacks precision in acoustic signal visualization. We used Praat to overcome this limit.

Praat is the most used tool for speech analysis in research related to language and speech. It benefits from a large, active community contributing many scripts for specific purposes. Praat is a powerful tool for visualizing, editing and analyzing various aspects of speech such as voice quality, rhythm, or intonational features. While Praat has become more user-friendly over time, a training phase is still required to exploit its ample possibilities.

### 5.2.3. Future prospects for speech analysis

Our secondary goal was to provide a ready-to-use method for speech analysis. The method we propose is still limited by the fact it uses three different software programs, and by the fact it requires some computer skills. Furthermore, while this method considerably reduces time and biases, speech analysis remains a long process that may not be applicable to all patients in clinical practice. Further research should especially develop software combining all three tools (i.e., Praat, Whisper and SPPAS) for clinical purposes, such as the Batchalign automated pipeline (Liu et al., 2023) which focuses on other aspects of language such as semantics and morphosyntax.

## 6. Conclusion

We aimed to distinguish breast cancer survivors with a cognitive complaint from those without and healthy controls measuring pause duration. For this particular dataset, we had hypothesized we would find longer silent pauses, filled pauses and sustained vowels for breast cancer participants with a cognitive complaint than for breast cancer participants without any complaint and healthy controls, based on previous studies from our team. We did find a significant difference in silent pause duration across the three participant groups, with post-hoc comparisons showing differences between cancer patients with a cognitive complaint and healthy controls, but no difference concerning cancer patients without a cognitive complaint. No difference was found across groups for filled pause duration and sustained vowel duration. In agreement with other studies on subtle cognitive impairment, our results support the claim that silent pauses are more indicative of a higher processing load than voiced disfluencies.

We proposed a detailed semi-automatized method for speech analysis using open source, off-line tools to deal with clinical data. This methodology speeds up annotation while protecting the data. We applied this methodology to a dataset of patients with Cancer-Related

Cognitive Impairment. This method is intended to help the diagnosis of subtle cognitive impairment via prosody. However, while our method and tutorials are ready to use, our protocol still relies on the use of several pieces of software. This calls for the development of automated pipelines combining different tools in order to make speech analysis even more applicable in clinical practice.

## Funding

Amélie B. Richard is financed by a Ph.D. grant awarded by Paul-Valery Montpellier 3 University and funded by the French Ministry of Higher Education (Ministère de l'Enseignement Supérieur et de la Recherche - MESRI).

## Ethics approval

The present study is part of three larger projects. The first project aims to evaluate the effectiveness of a physical rehabilitation program offered to women with breast cancer who have completed the main phase of their treatments, and the second one assesses the effectiveness of a cognitive rehabilitation program for individuals with subtle cognitive impairment due to stroke or cancer. They both have been approved by the ethics committee of the Hospices Civils de Lyon (respectively n°22-5009 and n°22-121). The third project focuses on cognitive impairment induced by cancer and its treatment in women who have had breast cancer, and was validated by the ethics committee Comité de Protection des Personnes Est-III in August 2023.

## Consent to participate

Participants have provided written informed consent to participate.

## Consent for publication

Participants have provided written informed consent to publish their data.

## CRediT authorship contribution statement

Amélie B. Richard: Writing - review & editing, Writing - original draft, Visualization, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Manon Lelandais: Writing - review & editing, Writing - original draft, Conceptualization. Sophie Jacquin-Courtois: Supervision, Resources.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

The authors would like to thank Farha Gheurbi and Marion Buchy for performing the inter-annotator agreement.

## Availability of data and materials

Our speech dataset and participant demographics are not available due to medical rules. Our full protocol (Pause_analysis_protocol) and Whisper install tutorial (Whisper_install) are available in the Speech analysis repository: https://osf.io/q8vyb/?view_only=93d5c4b21abe424b8aed4803df5b87c1
<figure><img src="image_7.png" /></figure>

Amélie B. Richard is a certified Speech-language Pathologist and holds a Ph.D. in Language Science (Université de Montpellier Paul Valéry). Her research focuses on speech markers of subtle cognitive impairment, particularly in cancer patients, with a special interest in developing automatic speech-based diagnosis tools.

<figure><img src="image_8.png" /></figure>

Sophie Jacquin-Courtois is an M.D. in Physical Medicine and Rehabilitation and holds a Ph.D. in Neuropsychology. She currently works at the Neurology Rehabilitation unit of the Henry Gabrielle Hospital (Hospices Civils de Lyon) where she develops programs for women cancer survivors.

<figure><img src="image_9.png" /></figure>

Manon Lelandais is an Associate Professor in Language Science (Université Paris Cité). She studies the interplay between the verbal, vocal, and gestural resources we use when we talk, to deepen our understanding of the real-time mechanisms of speech production.