Singing Voice Therapy Revisited

Introduction. Singing is a type of sportive activity and, like sports medicine, professional voice medicine is interested in the habilitation and rehabilitation of the vocal performer. The vocal needs of the professional vocal performer may not be similar to other professional or non-professional voice users. Like a professional athlete, a vocal performer’s ability to perform for many decades at a high level will be enhanced by basing artistic and lifestyle decisions on a scientifically sound foundation. Objective. The aim of this study is to present a multidimensional introduction to the methods of SVT, incorporating the principles of sport and exercise medicine, and physical therapy and rehabilitation. Reflection. Singing voice therapy needs to provide answers to “what”, “why”, “how”, and “when” questions. SVT must first correctly identify the problem, leading to the “how to do” solutions for a wide variety of cases, followed by a schedule of prescribed activities including answers to the “why” question (which exercise relates to which muscle). The periodization and motor learning principles provide a temporal answer to the “when” question when developing habilitation and/or rehabilitative protocols. Conclusion. Singing is not only an artistic expression, but also a sportive performance. The clinical approach to professional voice is a multidimensional and multi-layered team effort. All practices are structured by blending scientific and pedagogical knowledge.

Classifying patient and professional voice in social media health posts

Background Patient-based analysis of social media is a growing research field with the aim of delivering precision medicine but it requires accurate classification of posts relating to patients’ experiences. We motivate the need for this type of classification as a pre-processing step for further analysis of social media data in the context of related work in this area. In this paper we present experiments for a three-way document classification by patient voice, professional voice or other. We present results for a convolutional neural network classifier trained on English data from two different data sources (Reddit and Twitter) and two domains (cardiovascular and skin diseases). Results We found that document classification by patient voice, professional voice or other can be done consistently manually (0.92 accuracy). Annotators agreed roughly equally for each domain (cardiovascular and skin) but they agreed more when annotating Reddit posts compared to Twitter posts. Best classification performance was obtained when training two separate classifiers for each data source, one for Reddit and one for Twitter posts, when evaluating on in-source test data for both test sets combined with an overall accuracy of 0.95 (and macro-average F1 of 0.92) and an F1-score of 0.95 for patient voice only. Conclusion The main conclusion resulting from this work is that combining social media data from platforms with different characteristics for training a patient and professional voice classifier does not result in best possible performance. We showed that it is best to train separate models per data source (Reddit and Twitter) instead of a model using the combined training data from both sources. We also found that it is preferable to train separate models per domain (cardiovascular and skin) while showing that the difference to the combined model is only minor (0.01 accuracy). Our highest overall F1-score (0.95) obtained for classifying posts as patient voice is a very good starting point for further analysis of social media data reflecting the experience of patients.

Validation and Classification of the 9-Item Voice Handicap Index (VHI-9i)

The international nine-item Voice Handicap Index (VHI-9i) is a clinically established short-scale version of the original VHI, quantifying the patients’ self-assessed vocal handicap. However, the current vocal impairment classification is based on percentiles. The main goals of this study were to establish test–retest reliability and a sound statistical basis for VHI-9i severity levels. Between 2009 and 2021, 17,660 consecutive cases were documented. A total of 416 test–retest pairs and 3661 unique cases with complete multidimensional voice diagnostics were statistically analyzed. Classification candidates were the overall self-assessed vocal impairment (VHIs) on a four-point Likert scale, the dysphonia severity index (DSI), the vocal extent measure (VEM), and the auditory–perceptual evaluation (GRB scale). The test–retest correlation of VHI-9i total scores was very high (r = 0.919, p < 0.01). Reliability was excellent regardless of gender or professional voice use, with negligible dependency on age. The VHIs correlated best with the VHI-9i, whereas statistical calculations proved that DSI, VEM, and GRB are unsuitable classification criteria. Based on ROC analysis, we suggest modifying the former VHI-9i severity categories as follows: 0 (healthy): 0 ≤ 7; 1 (mild): 8 ≤ 16; 2 (moderate): 17 ≤ 26; and 3 (severe): 27 ≤ 36.

Professional written voice “in flux”: the case of social work

Contemporary professional social work can be characterised by increased textualisation (after Iedema, Rick & Hermine Scheeres. 2003. From doing work to talking work: Renegotiating knowing, doing and identity. Applied Linguistics 24(3). 316–337) with written texts mediating most action. At the same time, writing, as a key dimension to social workers’ practice and labour, is often institutionally unacknowledged, becoming visible primarily when identified as a “problem.” This paper draws on a three year nationally funded UK-based research project to offer a situated account of contemporary professional social work writing, challenging dominant institutional orientations to writing in professional practice. The paper outlines the specific ways in which social work practices, including writing, can be characterised as being ‘in flux’. Drawing on ethnographic data and adopting a Bakhtinian (Bakhtin, Mikhail. 1981. Discourse in the novel. In Michael Holquist (ed.), The dialogic imagination. Four essays by M. Bakhtin, trans. C. Emerson and M. Holquist, 259–422. Austin: University of Texas Press; and Bakhtin, Mikhail. 1986. The problem of speech genres. In Caryl Emerson & Michael Holquist (eds.), Speech genres and other late essays, trans. V. W. McGee, 60–102. Austin: University of Texas Press) oriented approach to voice, the paper explores the entextualisation of three specific social work texts, focusing in particular on critical moments (after Candlin, Christopher N. 1987. Explaining moments of conflict in discourse. In Ross Steele & Terry Treadgold (eds.), Language topics: Essays in honour of Michael Halliday, 413–429. Amsterdam: John Benjamins; Candlin, Christopher N. 1997. General editor’s preface. In Britt Louise Gunnarsson, Per Linell & Bengt Nordberg (eds.), The construction of professional discourse, viii–xiv. London: Longman). These critical moments offer insights into key problematics of social work writing, in particular the tensions around professional voice and discourse. The paper concludes by arguing for an articulation of professional social work writing which takes account of the dialogic nature of language and the discoursal challenges experienced in everyday practice.

Classifying Patient and Professional Voice in Social Media Health Posts

Background: Patient-based analysis of social media is a growing research field with the aim of delivering precision medicine but it requires accurate classification of posts relating to patients’ experiences. We motivate the need for this type of classification as a pre-processing step for further analysis of socialmedia data in the context of related work in this area. In this paper we present experiments for a three-way document classification by patient voice, professional voice or other. We present results for a Convolutional Neural Network classifier trained on English data from two different data sources (Reddit and Twitter) and two domains (cardiovascular and skin diseases). Results: We found that document classification by patient voice, professional voice or other can be done consistently manually (0.92 accuracy). Annotators agreedroughly equally for each domain (cardiovascular and skin) but they agreed more when annotating Reddit posts compared to Twitter posts. Best classification performance was obtained when training two separate classifiers for each data source, one for Reddit and one for Twitter posts, when evaluating on in-source test data for both test sets combined with an overall accuracy of 0.95 (and macro-average F1 of 0.92) and an F1-score of 0.95 for patient voice only.Conclusion: The main conclusion resulting from this work is that using more data for training a classifier does not necessarily result in best possible performance. In the context of classifying social media posts by patient and professional voice, we showed that it is best to train separate models per data source (Reddit andTwitter) instead of a model using the combined training data from both sources. We also found that it is preferable to train separate models per domain (cardiovascular and skin) while showing that the difference to the combined model is only minor (0.01 accuracy). Our highest overall F1-score (0.95) obtained for classifying posts as patient voice is a very good starting point for further analysis of social media data reflecting the experience of patients.