Using fundamental frequency of cancer survivors’ speech to investigate emotional distress in out-patient visits

Abstract

2 Highlights  Emotions in clinical conversations of cancer patients studied by a coding system and speech prosody  Emotional energy f0 was associated with cues and concerns as coded by VR-CoDES system  An additional aid to study emotional speech with potential to reveal hidden content and meaning AbstractObjective: Emotions, are in part conveyed by varying levels of fundamental frequency of voice pitch (f0). This study tests the hypothesis that patients display heightened levels of emotional arousal (f0) during Verona Coding Definitions of Emotional Sequences (VR-CoDES) cues and concerns versus during neutral statements. Methods:The audio recordings of sixteen head and neck cancer survivors' follow-up consultations were coded for patients' emotional distress. Pitch (f0) of coded cues and concerns, including neutral statements was extracted. These were compared using a hierarchical linear model, nested for patient and pitch range, controlling for statement speech length. Utterance content was also explored. Results:Clustering by patient explained 30% of the variance in utterances f0. Cues and concerns were on average 13.07 Hz higher than neutral statements (p = 0.02). Cues and concerns in these consultations contained content with a high proportion of recurrence fears. Conclusion:The present study highlights the benefits and challenges of adding f0 and potential other prosodic features to the toolkit of coding emotional distress in the health communication setting. Practice implications:The assessment of f0 during clinical conversations can provide additional information for research into emotional expression.3

Estimation of Voice Perturbation Measures Using Signal Processing Algorithms

“…A recent study claims that the objective voice parameters are used to investigate the emotional distress of cancer patients [3]. The outcomes from this study show proof that any abnormal variation in voice fundamental frequency and amplitude depicts the presence of laryngeal pathology [4].…”

“…RAP stands for Relative Average Perturbation, which can be estimated as shown in equation [7]. It is represented in percentage as shown in equation (3).…”

Automated screening for distress: A perspective for the future

“…A comparative study (Alghowinem, Goecke, Wagner, Epps et al, ) using acoustic and prosodic features to detect depression in spontaneous speech found that voice features such as intensity, root mean square and loudness perform best to detect depression in the data set. Other studies (Dham, Sharma, & Dhall, ; Kandsberger, Rogers, Zhou, & Humphris, ; Lopez‐Otero, Docio‐Fernandez, Abad, & Garcia‐Mateo, ; Salekin, Eberle, Glenn, Teachman, & Stankovic, ; Scherer, Morency, Gratch, & Pestian, ) also exploited different machine learning techniques and suggested that the speech can be effectively utilised to detect distress and related conditions.…”

“…Authors found that voice features such as intensity, root mean square, and loudness performed best to detect depression in the dataset. Other studies (for example [79]- [83]) also exploited different machine learning techniques and suggested that the speech can be effectively utilised to detect distress and related conditions.…”

Predictors of weight discussion in primary care consultations: A multilevel modeling approach

“…Previous research has attributed differences in healthcare communication to variability in patient and healthcare practitioner demographic, psychological and cultural characteristics [25][26][27]. Multilevel modelling approaches have previously been employed to examine patient-practitioner communication in a variety of clinical settings, including oncology [28], dentistry [29], and primary care [22,30,31].…”