"So . . . my child . . . " – How Child ADHD Influences the Way Parents Talk

“So . . . my child . . . ” – How Child ADHD Influences the Way Parents Talk Anika A. Spiesberger1, Andreas Triantafyllopoulos1, Alexander Kathan2, Anastasia Semertzidou1, Caterina Gawrilow3,7,8, Tilman Reinelt4,7, Wolfgang A. Rauch5, Bj¨ orn W. Schuller1,2,6 1CHI – Chair of Health Informatics, MRI, Technical University of Munich, Germany 2Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany 3Department of Psychology, University of Tuebingen, Germany 4Department of Neonatology, University of Zurich, Switzerland 5Department of Special Education, Ludwigsburg University of Education, Germany 6GLAM – Group on Language, Audio, & Music, Imperial College London, UK 7IDeA – Center for Research on Individual Development and Adaptive Education of Children at Risk, Frankfurt am Main, Germany 8DZPG – German Center for Mental Health [email protected] Abstract Attention-deficit/hyperactivity disorder (ADHD) exerts a psychological burden not only on affected individuals but also on their social support systems. Of particular interest are the parents, who often face challenges related to their child’s condition, including its impact on their own mental well-being. The interaction among the child’s symptomatology, parental mental health, and the parent-child relationship is a crucial area of investigation. Expressed Emotion (EE), as assessed through the Preschool Five Minute Speech Sample (PFMSS), serves as a valuable measure. However, manual annotation of EE can be cumbersome and impractical for continuous monitoring. To address this, we propose leveraging machine learning methods. This study presents an initial exploration into predicting children’s ADHD diagnosis using linguistic and paralinguistic features derived from the PFMSS. Despite achieving a UAR score of 67.1 %, our results have not surpassed the performance of manually annotated EE. Index Terms: attention-deficit/hyperactivity disorder, parentchild relationship, expressed emotion, computer audition, computational paralinguistics, linguistics 1. Introduction Attention-deficit/hyperactivity disorder (ADHD) affects a considerable portion of the population, with a prevalence of 5.9 % among youth worldwide [1]. As the name suggests, the American Psychiatric Association [2] defines ADHD as “a persistent pattern of inattention and/or hyperactivity-impulsivity that interferes with functioning or development” [p. 59]. Symptoms include ‘difficulty sustaining attention’, ‘difficulty organising tasks’, and ‘forgetfulness in daily activities’ (predominantly inattentive presentation) and/or ‘difficulty waiting for one’s turn’, ‘fidgeting with hands or feet’, and ‘excessive talking’ (predominantly hyperactive/impulsive presentation). Like many mental disorders, ADHD symptoms impact not only individuals but also their family and social functioning [3]. One extensively studied aspect is the heightened stress experienced by parents of children with ADHD. A meta-analysis by Theule et al. [4] highlights significantly increased stress levels among these parents compared to nonclinical controls. According to Leitch et al. [5], parents attribute this elevated stress directly to their children’s ADHD behaviour. In addition, studies indicate that children’s behavioural problems due to ADHD, as assessed by the Child Behaviour Checklist (CBCL), are correlated with both parenting stress and levels of parental depression [6, 7]. This increased psychological burden put on parents of children with ADHD is likely to influence the parent-child relationship. Studies reveal disparities in Expressed Emotion (EE) (i. e., “negative attitudes (. . . ) demonstrated by family members toward a person with a mental disorder”1) between parents of children with and without ADHD, with negative attitudes more prevalent in the former [8]. In their study, Perez et al. [8] used the Five Minute Speech Sample (FMSS), a method to investigate the parent-child relationship and its emotional quality, and found more negative Initial Statements, less Warmth, and a worse overall Relationship with the child in parents of children with ADHD. Additionally, they counted significantly fewer Positive Comments and significantly more Negative Comments. Daley et al. [9] and Psychogiou et al. [10] also demonstrated positive correlations between child ADHD symptoms and critical expression of the parent towards the child using the Preschool FMSS (PFMSS) and the FMSS, respectively. On the other hand, parent-child relationship and parental EE influence child ADHD symptoms. Musser et al. [11] demonstrated that high parental criticism inhibits the decline of ADHD symptoms and Keown [12] showed the predictive value of maternal warmth on ADHD. Given this bidirectional influence between parenting and child behaviour [13, 14], a potential negative feedback loop can worsen the child’s condition. Intervention programs, therefore, tackle not only the children’s symptoms but also include training and education for parents [15]. However, studies measuring the success of such programs currently use a wide variety of outcome measures, such as ADHD symptoms, conduct problems, parental self-esteem, parental well-being, parental stress, and parenting [16]. This heterogeneity affects the comparability of studies and limits the possibility of continuous assessment, as many methods rely on clinical interviews or structured observation [16]. We suggest using automatic measurement methods to pre1https://dictionary.apa.org/expressed-emotion Interspeech 2024 1-5 September 2024, Kos, Greece 2010 10.21437/Interspeech.2024-744 dict a child’s ADHD diagnosis from a parent’s speech sample. In addition to the possible utilisation as a pre-screening instrument, this could be used for longitudinal observations, enable the timely detection of improvement (or deterioration) in this negative feedback loop, and improve the quality of care. In the present contribution, we focus exclusively on the speech of parents collected during a PFMSS study to distinguish between parents of children with and without ADHD. Recent studies have explored machine-learning methods to predict EE scores from the (P)FMSS, achieving promising results. Movaghar et al. [17] classified high and low levels of criticism in mothers of schizophrenic adults using transcribed FMSS and Random Forests (AUROC: 0.75). Mirheidari et al. [18] used textual and acoustic features and different machine-learning methods to predict the Warmth variable of EE. With acoustics alone, they achieved their best result with the EGEMAPS and COMPARE 2010 feature sets (F1score: 59.9). Combining these features with text improved their results only marginally. Weintraub et al. [19] tried to predict the overall EE rating and the subscale scores using LIWC features and a Support Vector Machine. They achieved accuracy scores between 75.2 % and 81.8 % for high versus low EE, criticism, Warmth, and emotional overinvolvement. Going beyond previous work, we explore a variety of paralinguistic (prosodic, acoustic, and temporal) and linguistic features and their suitability for this task. We compare the predictive values of our features with that of the EE variables and look at how they could complement each other. Importantly, we also investigate the interplay between features and time; given that the PFMSS results in five-minute speech samples, we analyse the temporal dynamics of the features. This is especially interesting given that one subscale of the EE is the Initial Statement of the (P)FMSS. More broadly, our work opens new avenues towards understanding how mental disorders influence the social support system of patients (in this case, parents), a vital and insofar underexplored aspect of digital monitoring. The remainder of this paper is organised as follows: In the first step, we will introduce the dataset and explain the extracted feature sets. We will then continue with the methodology used and the obtained results before we finish with a discussion of the results, the implications, and the limitations. 2. Dataset We use part of a German dataset collected to study ADHD. The initial study included 115 parent-child dyads, children aged between 7 and 13 years, who completed various questionnaires, tasks, and interviews. Children in the ADHD group had a previous clinical diagnosis, which was corroborated using a structured interview based on ICD-10 criteria [20] and multiple symptom checklists. In the control group, children were only included if they had never been diagnosed with ADHD and their symptom scores in the interview and checklists were below the cutoff. For a full description, see Wirth et al. [21]. For this study, we use only the recordings of the PFMSS in which parents are encouraged to openly discuss their child, sharing their thoughts, feelings, and attitudes towards them while also elaborating on their relationship, uninterrupted, for five minutes. The average duration of the speech samples included in this study is 4.5 min (range = [2.0; 5.3]). In addition to the audio files, the dataset provides transcriptions of the interviews as well as manually annotated EE. As textual transcriptions, audio, and diagnostic results were not available for every dyad, we only included 77 parent interviews in our analysis (31 parents of children with A(DHD), 29 of these female; 46 C(ontrol), 41 female). The EE annotation, using the coding scheme from [9], was performed by two independent, blind raters. For every interview, the annotations include one value each for the Initial Sentence (MA= 0.45;MC= 0.62), Relation (MA= 1.00; MC= 0.85), Warmth (MA= 0.90;MC= 0.92), Positive Comment (MA= 3.66;MC= 4.13), and Negative Comment (MA= 2.47;MC= 0.85). 3. Methodology We used PYTHON-V3.10.11 for all preprocessing and analysis. Preprocessing. As paralinguistic features are generally more sensitive to changes for smaller units of analyses, our first step was to segment the data. For pragmatic reasons, we used the manual transcriptions to identify different prosodic phrases, denoted by punctuation characters, assuming that this is how the annotators perceived pauses. These transcriptions were uploaded to MAUS for forced alignment [22, 23], yielding the phrases we utilised as our unit of analysis. Feature Sets. From each interview, we extracted five different feature sets. Longitudinal Features were constructed using the time stamps for each phrase. They include the number of phrases as well as the total (P), mean (µ), standard deviation (σ), and slope (m) of the phrase duration (Phraset) and the duration of pauses between phrases (Pauset). We calculated these once for the entire interview and then separately for each third of the phrases by dividing the total number of phrases per person into three equal parts (1st,2nd, and 3rd part). Unlike all other feature sets, this results in a single feature vector per interview. The Linguistic Inquiry and Word Count (LIWC) [24] is a text analysis software tool that was applied to each phrase, using the most recent German version [25]. The extracted feature set contains a range of linguistic and psychological categories, including word categories (e. g., pronouns, prepositions, verbs, adverbs, conjunctions), linguistic dimensions (e. g., word count, word length, sentence length), and psychological dimensions (e. g., positive and negative emotions, cognitive processes, social processes, emotional tone). Arousal, Valence, and Dominance (AVD) were predicted using a state-of-the-art, large WAV2VEC2.0 model, which has been publicly released2[26]. These values are expected to capture an estimate of the parent’s emotions throughout the interview, thus lending more insight into their evolving state. WAV2VEC2.0 Embeddings: We additionally extracted learnt embeddings from the penultimate layer of the same WAV2VEC2.0 model. While they are more opaque than their output, we expect them to capture more relevant information and have higher predictive power. The extended Geneva Minimalistic Acoustic Parameter Set (EGEMAPS)[27] was extracted from the audio file for each phrase using the OPENSMILE-V2.4.1 toolkit [28]. It comprises 88 acoustic signal descriptors, encompassing 18 lowlevel descriptors such as pitch, jitter, formants, shimmer, and loudness. Various functionals are applied to these descriptors, including arithmetic mean, standard deviation, and percentiles. Additionally, there are six temporal features, such as the mean length of voiced and unvoiced regions, and seven cepstral parameters, such as MFCC and spectral flux. 2https://huggingface.co/audeering/wav2vec2-large-robust-12-ftemotion-msp-dim 2011 Table 1: Mann-Whitney U test results for features showing at least a small effect (Common Language Effect Size (CLES) >58.4 %). Mean (M) and standard deviation (SD) for the two groups, as well as pvalues, are provided. ADHD Control CLES Feature M(SD)M(SD) [%] p Critical Comment 2.52 (1.50) 0.87 (0.88) 83.3 < .001* PhrasetP(2nd part) 81.95 (15.17) 91.42 (14.68) 68.0 .008 Phrasetµ(2nd part) 2.47 (0.59) 2.89 (0.4) 66.6 .014 Phrasetm(3rd part) 0.02 (0.04) 0.01 (0.08) 65.5 .022 PausetP(1st part) 11.89 (6.60) 8.09 (4.45) 64.0 .061 Phrasetσ(2nd part) 1.87 (0.80) 2.19 (0.89) 62.7 .064 PausetP38.41 (23.02) 29.59 (18.58) 62.6 .064 Pausetµ(1st part) 0.35 (0.19) 0.27 (0.19) 60.8 .111 Phrasetm(2nd part) -0.01 (0.02) -0.00 (0.02) 60.6 .118 F3 frequency µ2614 (187) 2578 (212) 59.2 < .001* PhrasetP269.99 (23.92) 277.95 (21.45) 59.1 .179 Pausetσ(1st part) 0.52 (0.35) 0.42 (0.25) 58.9 .189 Initial Statement 0.50 (0.52) 0.62 (0.46) 58.8 .147 PausetP(3rd part) 16.02 (20.00) 12.19 (12.89) 58.8 .192 Pausetm(1st part) -0.00 (0.00) 0.00 (0.00) 58.8 .192 Note: pkrit < .001 for all after Bonferroni correction. * = significant Comparative Analysis. In the first step, we performed comparative analyses for all features (except WAV2VEC2.0 embeddings) and for the EE subscales to examine differences in distributions between ADHD and control groups. We utilised MannWhitney U(MWU) tests with an alpha level of 0.05 and corrected for multiple testing using the Bonferroni-Holm method. Due to the controversy of pvalues, we report features showing at least a small effect (greater than 58.4 %) using the CommonLanguage Effect Size (CLES), which indicates “the probability that a score sampled at random from the first population will be greater than a score sampled at random from the second” [29, p. 101]. Classification. Afterwards, we employed two distinct classifiers: SUPPORT VECTOR MACHINE (SVM) using SKLEARNV1.4.0 and EXTREME GRADIENT BOOSTING (XGBOOST) with XGBOOST-V2.0.3. After feature standardisation, these classifiers were trained on the EE subscales and each feature set individually, as well as on a combined feature set including all but the EE annotations. The hyperparameter optimisation and training were done using nested cross-validation (CV), employing three folds for the inner stratified CV and five folds for the outer stratified grouped CV. The hyperparameters considered during the search were kernel function ∈[linear, polynomial, sigmoid, radial basis function] and C ∈[0.0001, 0.001, 0.01, 0.1, 1] for SVM and maximum depth of tree ∈[3, 5, 7], learning rate ∈[0.1, 0.01, 0.001], and subsample size for each tree ∈[0.5, 0.7, 1] for XGBOOST. Evaluation. We used majority voting to create one prediction per interview per feature set. In addition, we also performed a majority vote across all feature sets (except EE). Model performance was evaluated using the unweighted average recall (UAR) score. This metric adjusts for imbalances in the sample class ratio by calculating the recall for each class and then averaging them without applying any weights based on class size. We include the 95 % Confidence Intervals, computed by repeating the bootstrapping approach 1 000 times. In addition to the general model performance for each feature set, where possible, we calculated the UAR separately for the first and last phrases as well as for each third of the interview (1st,2nd, and 3rd part). In order to do so, we split the predictions for each person into three parts (each containing one-third of the phrases) and performed a majority vote. 12345678910 Segment 2 4 6 8 Phrase Duration [s] ADHD Control 12345678910 Segment 0 2 4 6 Pause Duration [s] ADHD Control Figure 1: Average phrase and pause duration throughout the interview. ADHD Control Combined Features exc. EE ADHDControl Expressed Emotions 14 13 15 35 15 20 25 30 35 Figure 2: Confusion matrix between the predictions based on Expressed Emotion and the best-performing speech-based model. This shows the disagreement between the two models. 4. Results The comparative analysis using the MWU tests yielded 49 significant results. However, only Critical Comment and the mean F3 Frequency exceeded the effect size threshold we set. Looking only at the effect sizes, we found 13 more features that showed at least a small (CLES >58.4 %) effect but that were not significant. An overview of these can be found in Table 1. As most are from the longitudinal feature set, Fig. 1 depicts the changes in phrase and pause duration across the interviews. As SVM outperformed XGBOOST for all feature sets, we only report the results for SVM. The UAR scores can be found in Table 2, which shows that only WAV2VEC2.0 embeddings and the combined feature set yield results above the chance level. However, using the manual-annotated EE achieved the best result. Therefore, in the next step, we compared the predictions of the EE and the combined feature set to inspect the potential for complementary use. The confusion matrix can be found in Fig. 2. 2012 Table 2: Unweighted average recall (UAR) scores with 95 % confidence intervals for SVM are given in %. UAR scores are given for the first and last phrases, the whole interview, and the 1st,2nd, and 3rd part of the interview using majority voting. All First phrase 1st part 2nd part 3rd part Last phrase Expressed Emotion 74.6 [64.2; 84.1] - - - - - Longitudinal 55.8 [45.5; 66.3] - - - - - LIWC 45.1 [37.0; 52.8] 51.5 [42.9; 61.2] 43.4 [35.9; 50.7] 44.0 [36.0; 51.5] 43.4 [35.9; 50.7] 48.2[38.3; 58.6] AVD 40.7 [32.3; 48.5] 43.9 [34.9; 53.5] 40.1 [31.5; 48.9] 40.7 [32.3; 48.5] 39.1 [29.9; 47.8] 43.9[34.8; 53.5] WAV2VEC2.0 64.9 [54.1; 75.7] 49.3 [38.5; 59.9] 65.5 [55.4; 75.4] 66.0 [55.4; 76.0] 62.2 [51.6; 73.1] 66.1 [55.5; 75.8] EGEMAPS 51.5 [41.3; 61.9] 54.2 [44.8; 63.4] 52.5 [42.3; 63.3] 48.2 [38.5; 58.3] 58.0 [47.8; 68.3] 46.1 [36.2; 56.6] Combined (exc. EE) 67.1 [55.7; 78.2] 54.1 [43.5; 64.9] 67.1 [55.7; 78.2] 64.4 [52.9; 75.2] 66.5 [55.6; 77.3] 60.1 [49.4; 70.4] Majority vote (exc. EE) 51.0 [42.0; 60.2] 45.1 [39.3; 51.0] 47.2 [39.8; 54.7] 48.3 [41.3; 55.6] 47.2 [39.8; 54.7] 50.5 [44.4; 56.9] Note: Scores not exceeding the chance level are in grey. 5. Discussion In this paper, we investigated the possibility of predicting ADHD diagnosis in children by using linguistic and paralinguistic features from parents’ PFMSS. Our results show that linguistic and paralinguistic features could not outperform the predictive capabilities of manually annotated EE. However, we were still able to achieve UAR scores above 67 % with a combined feature set. Moreover, as shown in Fig. 2, the predictions of the best speech-based model are highly complementary to the annotated EE, suggesting that they can form an alternative lens with which to analyse the impact of child ADHD on the parental conceptualisation of the child. In accordance with Wirth et al. [21], our analyses show a large and significant effect for Critical Comment and a small but not significant effect for Initial Statement. Both indicate higher values for the ADHD group compared to the control. We could not replicate any of the other differences in EE found by Daley et al. [9]. This could be a side-effect of the fact that the parents and children in our dataset were already in therapy, which may have resulted in better regulation of parents’ emotions and higher positive ideation of the parents in the ADHD group. Diving deeper into individual features, we found small effects for longitudinal features – phrase and pause duration – although they were not significant. This indicates that changes in speaking tempo may be correlated with negative ideation. In particular, parents of ADHD children show lower speaking duration and an increase in pauses at later stages of the interview. These differences could be due to a lack of positive ideation, necessitating time to think about positive comments to make. Future studies could aim to confirm this by inspecting the link between features and content. Regarding automatic performance, learnt neural network embeddings yield the highest UAR across all different stratifications. This is in contrast to the ‘standard practice’ of analysing interpretable linguistic (LIWC) or paralinguistic (EGEMAPS) features. Moreover, taking the output (AVD) predictions of WAV2VEC2.0, we obtain lower-than-chance performance. This suggests that there is information in parents’ speech about their child’s status but that this information requires novel, ‘deeper’ methods of analysis. Given the widespread use of LIWC and simple paralinguistic indicators in psychology research, our study highlights the promise of more modern approaches. Finally, while the annotation of the (P)FMSS and the result of the MWU test emphasise the Initial Statement, our results show that accuracy remains constant and the opening phrase shows no predictive power. This aligns with Wirth et al. [21], who found that annotators rated the Initial Statement neutral for most interviews. However, this might, in part, be caused by the fact that we segmented the interviews into phrases instead of sentences. Overall, our study shows that child ADHD can be detected in the parents’ voice. This is primarily possible using learnt embeddings from a deep neural network (DNN) trained for emotion prediction (but not from the emotion predictions themselves). Shallow features, namely LIWC and EGEMAPS, fail to distinguish between parents of ADHD children and control. Longitudinal features covering the timing information in and between utterances differ in how they manifest over time between the two groups, with shorter utterances and more pauses in the latter parts of the monologue for the ADHD parents. More broadly, our work focuses on an under-explored research area of speech science – that of longer monologues concerning a particular topic of (emotional) importance for the participants. This is a challenging problem, given that the participants’ attitude (in this case, about their child) is not clearly expressed in every single utterance. Instead, it manifests in more nuanced cues, such as the timing of utterances and features encoded in the learnt representations of a DNN. Our findings thus open up new avenues in the analysis of long-term monologues regarding emotional ideation about a particular topic – both in the context of ADHD and beyond. Limitations. As Starck et al. [30] demonstrated that around 43.1 % of the fathers and 33.8 % of the mothers of children with ADHD scored above the cut-off for current ADHD symptomatology, we need to be aware that this might have influenced the current study’s results. For future studies, it would be interesting to investigate how results change depending on the parents’ own ADHD symptomatology. 6. Conclusion Speech and Health — as part of this year’s conference theme, Speech and Beyond represents a growing area of research. In this paper, we delve into an emerging avenue: leveraging speech analysis to investigate the impact of mental disorders on the patients’ social support networks. By using linguistic, as well as prosodic, acoustic, and temporal features extracted from the PFMSS, we demonstrated the feasibility of predicting children’s ADHD diagnosis from their parents’ speech. We achieved a UAR score of 67.1 % for our combined feature set. In addition, we found differences in phrase and pause duration, especially in the second half of the interviews. To deepen our understanding of the influence of children’s diagnosis on parents, future research in this area will need to closer investigate the connections between symptomatology, parent-child relationship, and the broader familial and social dynamics. In this context, it will be especially important to keep in mind the high heritability of ADHD. 2013 7. 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