Voice Recordings Research Articles

Analysis of The Implementation of Countermeasure Policies Against Stunting

Introduction: Stunting is a major nutrition problem worldwide, especially in poor and developing countries. This problem leads to children's suboptimal brain, mental, and cognitive development. The stunting rate globally was 32.6% in 2000, and by 2017, around 150.8 million people were suffering from malnutrition and stunting. This research aims to determine the implementation of stunting prevention policies in the Puskesmas (Public Health Centre) Blang Cut working area.Methods: The research used a qualitative method with a descriptive approach to analyze the implementation of countermeasure policies to reduce stunting. The Health Belief Model was used as the theoretical framework. The methodological orientation of this research was discourse analysis. The study used an interview guide and a voice recorder to collect information from 9 informants.Results: Puskesmas Blang Cut has implemented several countermeasure policies to reduce stunting. These include increasing awareness about the importance of proper nutrition and hygiene, training healthcare workers on stunting prevention, and monitoring children's growth regularly. Implementing these policies has led to a significant reduction in the prevalence of stunting. However, some challenges still need to be addressed, such as increasing access to healthcare services and improving the quality of healthcare facilities.Conclusion: Communication factors related to implementing Countermeasure Policies in Stunting Reduction have been running well. The puskesmas has carried out all stunting reduction program activities, but the more dominant one is the Supplementary Feeding Program for those affected by stunting.

A Randomized Trial of Choice Architecture and Mailed Colorectal Cancer Screening Outreach in a Community Health Setting

Mailed outreach for colorectal cancer (CRC) screening increases uptake but it is unclear how to offer the choice of testing. We evaluated if the active choice between colonoscopy and fecal immunochemical test (FIT), or FIT alone, increased response compared with colonoscopy alone. This pragmatic, randomized, controlled trial at a community health center included patients between ages 50 and 74 who were not up to date with CRC screening. Patients were randomized 1:1:1 to the following: (1) colonoscopy only, (2) active choice of colonoscopy or FIT, or (3) FIT only. Patients received an outreach letter with instructions for testing (colonoscopy referral and/or an enclosed FIT kit), a reminder letter at 2 months, and another reminder at 3 to 5 months via text message or automated voice recording. The primary outcome was CRC screening completion within 6 months. Among 738 patients in the final analysis, the mean age was 58.7 years (SD, 6.2 y); 48.6% were insured by Medicaid and 24.3% were insured by Medicare; and 71.7% were White, 16.9% were Black, and 7.3% were Hispanic/Latino. At 6 months, 5.6% (95% CI, 2.8-8.5) completed screening in the colonoscopy-only arm, 12.8% (95% CI, 8.6-17.0) in the active-choice arm, and 11.3% (95% CI, 7.4-15.3) in the FIT-only arm. Compared with colonoscopy only, there was a significant increase in screening in active choice (absolute difference, 7.1%; 95% CI, 2.0-12.2; P= .006) and FIT only (absolute difference, 5.7%; 95% CI, 0.8-10.6; P= .02). Both choice of testing and FIT alone increased response and may align with patient preferences. clinicaltrials.gov NCT04711473.

Design and Development of an Oral English Evaluation System Based on Speech Recognition Technology and A Scoring Algorithm

Proficiency in oral English plays a significant role in different professional and academic settings. However, evaluating oral English proficiency precisely is challenging because of the subjective nature of oral communication. In this study, we developed an innovative Oral English Evaluation System (OEES) by combining the efficiencies of speech recognition technology and scoring algorithms. Initially, an intensive oral English database was collected, containing numerous persons' voice recordings. The collected voice recordings are pre-processed to remove the background noise, ensuring clarity in the voice signals. Then, an automatic speech recognition algorithm was developed using the recurrent neural network (RNN) to transcribe voice signals into text. This module trains the system to map the voice signals to their corresponding textual characteristics. Finally, an adaptive scoring algorithm was incorporated into the designed OEES to evaluate the oral English proficiency of the individuals. The adaptive scoring algorithm considers different factors like fluency, grammar, pronunciation, and vocabulary for evaluating the individual's oral English proficiency as "excellent," "good," and "poor." The presented framework was modeled in Python and validated across diverse natural English-speaking databases. The experimental results are assessed in terms of accuracy, precision, recall, and error rate. The implementation outcomes suggest that the proposed OEES framework accurately evaluates oral English proficiency.

Automatic Spontaneous Speech Analysis for the Detection of Cognitive Functional Decline in Older Adults: Multilanguage Cross-Sectional Study.

The rise in life expectancy is associated with an increase in long-term and gradual cognitive decline. Treatment effectiveness is enhanced at the early stage of the disease. Therefore, there is a need to find low-cost and ecological solutions for mass screening of community-dwelling older adults. This work aims to exploit automatic analysis of free speech to identify signs of cognitive function decline. A sample of 266 participants older than 65 years were recruited in Italy and Spain and were divided into 3 groups according to their Mini-Mental Status Examination (MMSE) scores. People were asked to tell a story and describe a picture, and voice recordings were used to extract high-level features on different time scales automatically. Based on these features, machine learning algorithms were trained to solve binary and multiclass classification problems by using both mono- and cross-lingual approaches. The algorithms were enriched using Shapley Additive Explanations for model explainability. In the Italian data set, healthy participants (MMSE score≥27) were automatically discriminated from participants with mildly impaired cognitive function (20≤MMSE score≤26) and from those with moderate to severe impairment of cognitive function (11≤MMSE score≤19) with accuracy of 80% and 86%, respectively. Slightly lower performance was achieved in the Spanish and multilanguage data sets. This work proposes a transparent and unobtrusive assessment method, which might be included in a mobile app for large-scale monitoring of cognitive functionality in older adults. Voice is confirmed to be an important biomarker of cognitive decline due to its noninvasive and easily accessible nature.

Structural, Semantic, and Pragmatic of Code-Switching in the Speech of Secondary School and University Students – A Secondary Publication

Code-switching is a complex phenomenon that varies in structure, semantics, and pragmatics in different communicative situations. The study aims to identify structural, semantic, and pragmatic characteristics of code-switching in the speech of Russian schoolchildren and University students learning English and German as foreign languages. The data are 157 utterances containing 165 code switches extracted from the oral spontaneous speech of 16 schoolchildren and 16 University students. Their bilingual speech samples were collected by means of non-participant observation and fixed with the help of a voice recorder for 32 hours (16 for schoolchildren and 16 for University students) from October to December 2021. Comparative analysis was used as the main method to interpret the features of schoolchildren’ and University students’ code-switching. The results of the research demonstrated that the most productive for schoolchildren were insertions proper and bare forms performing message-oriented pragmatic functions and intended to denote school objects associated with English classes. For University students most frequent structures were classical switches in the form of insertions properly performing message-oriented pragmatic functions. Semantically, code-switches among University students were more complicated since they were intended not only to denote specific University objects but also to express stylistically relevant shades of meaning. Though code-switching has already been studied within various frameworks, the correlation between structural, pragmatic, and semantic characteristics of code-switching in the speech of Russian-speaking school and University students has been under-researched, which provides originality of the present work.

Voice as an Indicator for Laryngeal Disorders Using Data Mining Approach

Introduction: Laryngeal disorders are a common health problem that affects people of all ages, genders, and races. One of the main symptoms of laryngeal disorders is changes in the voice, which can be used as an indicator for the presence of such disorders. In this paper, we present a data mining approach for using voice as an indicator for laryngeal disorders.Material and Methods: We collected a dataset of voice recordings from individuals with and without laryngeal disorders including 434 people from two clinical centers in Tehran. The dataset was created using a powerful signal processing program and then based on the difference between male and female voice, the dataset was separated into two datasets. Finally, a Deep Neural Network was implemented for modelling using Python programming language and F1-score, Accuracy, Sensitivity, Specificity, and AUC as the model’s evaluation metrics were reported.Results: Among all the acoustic features, 23 features were selected for the male dataset and 25 features for the female data set. For the male dataset the final model achieved F1-Score of 0.915 and Accuracy of 0.910. For the female dataset the result was 0.884 of F1-Score and 0.896 of Accuracy.Conclusion: Our results show that machine learning algorithms can accurately classify voice recordings into two groups: individuals with laryngeal disorders and those without. The high accuracy achieved by the algorithms suggests that voice can be used as an objective and automated diagnostic tool for laryngeal disorders.

Exploring Crop Choices: Benefits, Challenges, and Rationale Among Rwandan Farmers

Farming decisions on crop choices are guided by different factors including natural conditions, household needs, traditions, stakeholder recommendations, and productivity. The best decision varies for each farmer based on specific circumstances. There are both benefits and challenges in farmers’ crop growing experience. In Rwanda, agriculture employs 70% of the population, contributing 33% to the GDP across three main agricultural seasons. However, food and nutritional insecurity remain pressing issue affecting both human and economic progress. This study explored the rationale, benefits, and challenges of farmers’ choices. This study used a qualitative descriptive approach, conducting six focus group discussions (FGDs) in each participating district. Each FGD comprised 10 participants, ensuring gender balance. Recruitment was facilitated by local community health workers (CHWs), with participants providing informed consent. Trained data collectors utilized voice recorders to collect the data. The researchers transcribed the data verbatim, anonymized the data, and translated the same data into English. Data analysis revealed four key themes: reasons for cultivation, factors influencing crop choice, farmers’ livelihoods, and farming challenges. Findings highlight the need for holistic and context-specific solutions in Rwandan agricultural development, emphasizing stakeholder collaboration to support informed decision-making and sustainable agriculture.

Novel Method for Sealing Tracheostomies Immediately after Decannulation-An Acute Clinical Feasibility Study.

Tracheostomy decannulation leaves an iatrogenic passage in the upper airways. Inadequate sealing leads to pulmonary dysfunction and reduced voice quality. This study aimed to investigate the feasibility and impact of intratracheal tracheostomy sealing on laryngeal airflow and voice quality immediately after decannulation (ClinicalTrials.gov: NCT06138093). Fifteen adult, tracheostomized, intensive care unit patients were included from our hospital. A temporary, silicone-based sealing disc was inserted in the tracheostomy wound immediately after decannulation. Spirometry with measurement of forced vital capacity (FVC), forced expiratory volume in the first second (FEV1), and peak expiratory flow (PEF) were performed as measures of airway flow. Voice recordings were assessed using an equal appearing interval scale from 1 to 5. Median FVC, FEV1, PEF, and voice quality score with interquartile range (IQR) was 883 (510-1910) vs. 1260 (1005-1723) mL (p < 0.001), 790 (465-1255) vs. 870 (617-1297) mL (p < 0.001), 103 (55-211) vs. 107 (62-173) mL (p = 0.720), and 2 (1-2.5) vs. 4 (3-5) points (p < 0.001), respectively, with open tracheostomy vs. after sealing the tracheostomy with the intratracheal sealing disc. This feasibility study showed that tracheostomy sealing with the intratracheal disc was safe and led to immediate improvements in FVC, FEV1, and voice quality.

Experiences of women with gestational diabetes about fear of having diabetes in their babies: A qualitative study.

Gestational Diabetes Mellitus is an important public health problem that often occurs during pregnancy. This study aimed to reveal the experiences of women with gestational diabetes regarding the fear of having diabetes in their babies. A qualitative research method was carried out with a phenomenological approach. The interviews had a semi-structured form and were recorded on an online/face-to-face voice recorder, and thematic content analysis was performed on the MAXQDA22. Following the inclusion criteria, 12 women with gestational diabetes from the 2 hospitals in the study were included, and in this way, the study reached saturation. As a result of the interviews, 4 main themes and one subtheme were obtained from coding. The main themes were "sugar baby," "risky child," "raising a fearful baby," and "problematic gene carrier." From the main theme of "problematic gene carrier," the subtheme of "pregnancies with problematic genes" was created. This research sheds light on the problems women with gestational diabetes experience with themselves and their babies, and how they deal with these problems. Women with gestational diabetes try to accept and cope with the diagnosis. This research shows that the women were worried about both themselves and their babies. Illuminating the experiences of women with gestational diabetes is part of an integrative care approach that will help increase quality care and treatment in endocrine clinics. More qualitative studies are needed to learn more about the experiences of women with gestational diabetes in endocrine clinics.

A Voice User Interface on the Edge for People with Speech Impairments

Nowadays, fine-tuning has emerged as a powerful technique in machine learning, enabling models to adapt to a specific domain by leveraging pre-trained knowledge. One such application domain is automatic speech recognition (ASR), where fine-tuning plays a crucial role in addressing data scarcity, especially for languages with limited resources. In this study, we applied fine-tuning in the context of atypical speech recognition, focusing on Italian speakers with speech impairments, e.g., dysarthria. Our objective was to build a speaker-dependent voice user interface (VUI) tailored to their unique needs. To achieve this, we harnessed a pre-trained OpenAI’s Whisper model, which has been exposed to vast amounts of general speech data. However, to adapt it specifically for disordered speech, we fine-tuned it using our private corpus including 65 K voice recordings contributed by 208 speech-impaired individuals globally. We exploited three variants of the Whisper model (small, base, tiny), and by evaluating their relative performance, we aimed to identify the most accurate configuration for handling disordered speech patterns. Furthermore, our study dealt with the local deployment of the trained models on edge computing nodes, with the aim to realize custom VUIs for persons with impaired speech.

Caregivers' experiences and practices for malnourished children undergoing tuberculosis treatment in Eswatini.

Eswatini is one of the countries affected by malnutrition and tuberculosis (TB) and some cases remained untreated. These two conditions are major public health problems. This study aimed to explore and describe caregivers' experiences and practices of children's nutrition during treatment. Baylor College of Nursing Children's Foundation - Eswatini (BCMCF-SD). A qualitative study following a narrative design used purposive sampling to identify 12 caregivers of malnourished children and informed consent obtained. In-depth interview used semi-structured interview guide and digital voice recorder. Field notes were taken, transcribed, translated and analysed using NVivo version 11. Two themes emerged as home's nutritional situation and health facility's nutritional support. The study found that most of the caregivers gave children unbalanced diet, while those less than a year were mixed-fed. Some caregivers reported experience of lost breadwinners, unemployment and high number of children than what the family could afford. The caregivers' practices around food by prescription included inadequate supply of the ready-to-use therapeutic food and sharing of prescribed food supplies with other healthy children. During treatment, children's caregivers need short health education and support. The Ministry of Health in Eswatini should consider using some comic books to guide that. Moreover, upscale vocational training promotes entrepreneurship and agricultural activities. Association of malnutrition and TB outcomes has provided evidence-based information for more comprehensive integration between nutrition programmes and tuberculosis programmes. The study's findings contributed to the growing body of knowledge about the association between malnutrition and diagnosed drug-susceptible TB among children aged from 0 - 15 years.

Primary School Teachers’ Knowledge of Assistive Technologies for Students with Dyslexia and Dysgraphia in Abu Dhabi

This study aimed to determine the knowledge level of primary school teachers regarding their gender-based variations and the obstacles they face with using Assistive Technologies (ATs) for students suffering from dyslexia and dysgraphia in Abu Dhabi, UAE. For this purpose, a three-point Likert scale questionnaire was used to get teachers’ responses. The study’s main findings revealed that teachers have a high degree of knowledge about some of these technologies, such as highlighters, pen grips, voice recorders, and specific software applications. It also reveals significant gender differences in teachers’ knowledge of some of these technologies, with male teachers being more familiar with voice-based tools and female teachers being more familiar with visual and tactile tools. The most significant challenge was the short class timings, followed by the teachers’ lack of knowledge and training about the relevant ATs. This prevented primary school teachers from knowing ATs for students with dyslexia and dysgraphia. The findings contributed to the limited literature on gender knowledge levels and obstacles faced by teachers in using ATs for students with dyslexia and dysgraphia. Further, future directions for research are suggested.

Automatic Classification of Stegano files using the Features of Natural Language Processing and Hybrid Deep Learning Algorithms

The corporate sector has adopted communication as its principal medium due to the improvements of the digital era. In the past, in-person meetings, purchases, settlements, business talks, and profile-taking were all done. However, everything has gone digital in the present period. Over the last several years, there has been a noticeable exponential rise in the amount of complicated texts and documents that require a deeper comprehension of machine learning techniques in order to recognize languages in a variety of applications. Outstanding results in the processing of natural languages have been obtained by a number of Artificial Intelligent techniques. A key component of machine learning and deep learning techniques' efficacy is their capacity to understand intricate models and non-linear connections in data. Choosing the right architecture, frameworks, and algorithms for classifying input, like text files, managing audio and video files, however, is a difficult undertaking. The proposed work's goal is to identify text, audio messages, chatbots, and smart records using natural language processing. Using a hybrid deep learning approach, text, voice, and video recordings are used as inputs to be classified. Problem Synopsis: With contact becoming more and more important to business, companies have created advanced NLP programs. Through chatbots, digital records, phone conversations, and messages, these NLP swiftly fulfil human desires. Customer preferences, desires, and demands have been found to be more strongly impacted by the ease of communication and connection. Today's service providers use chatbots, digital records, messaging apps, email, and phone conversations as their main methods of communication for almost all of their trade channels of preference, client queries, and transactions. Proposed Method: The study shows how input stegano file is processed automatically based on user reactions, text message replies, and audio record identification during communications using text content, voice messages, and audio using the features of Natural Language Processing and hybrid Deep Learning Algorithms

A novel speech analysis algorithm to detect cognitive impairment in a Spanish population.

Early detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study presents preliminary data on a new screening technology using automated voice recording analysis software in a Spanish population. Data were collected from 174 Spanish-speaking individuals clinically diagnosed as cognitively normal (CN, n = 87) or impaired (mild cognitive impairment [MCI], n = 63; all-cause dementia, n = 24). Participants were recorded performing four common language tasks (Animal fluency, alternating fluency [sports and fruits], phonemic "F" fluency, and Cookie Theft Description). Recordings were processed via text-transcription and digital-signal processing techniques to capture neuropsychological variables and audio characteristics. A training sample of 122 subjects with similar demographics across groups was used to develop an algorithm to detect cognitive impairment. Speech and task features were used to develop five independent machine learning (ML) models to compute scores between 0 and 1, and a final algorithm was constructed using repeated cross-validation. A socio-demographically balanced subset of 52 participants was used to test the algorithm. Analysis of covariance (ANCOVA), covarying for demographic characteristics, was used to predict logistically-transformed algorithm scores. Mean logit algorithm scores were significantly different across groups in the testing sample (p < 0.01). Comparisons of CN with impaired (MCI + dementia) and MCI groups using the final algorithm resulted in an AUC of 0.93/0.90, with overall accuracy of 88.4%/87.5%, sensitivity of 87.5/83.3, and specificity of 89.2/89.2, respectively. Findings provide initial support for the utility of this automated speech analysis algorithm as a screening tool for cognitive impairment in Spanish speakers. Additional study is needed to validate this technology in larger and more diverse clinical populations.

Outcomes of an integrated STEM with design thinking module on preschoolers’ engineering practices

Research indicates that utilizing engineering design processes such as design thinking (DT) to integrate science, technology, engineering, and mathematics (STEM) disciplines yields positive outcomes. However, there is limited study on the effects of STEM education on preschoolers’ engineering practices. This case study evaluation examined the outcomes of an integrated STEM with DT module on preschoolers’ engineering practices in a private preschool in Malaysia. Two preschool teachers facilitated the learning of twenty preschoolers in two classes daily over four weeks. Data was collected through interviews and direct classroom observations, including fieldnotes, students’ artefacts, photos, voice, and video recordings. The qualitative data were analyzed inductively through thematic analysis. The findings indicated that the preschoolers engaged in numerous engineering practices while they actively participating in learning tasks. During their efforts to solve problems using DT process, they showed compassion for the characters in the stories and successfully defined the problem. The findings also highlighted the preschoolers’ ability to design and sketch their ideas. They demonstrated proficiency in constructing, testing, analyzing and evaluating their designs, as well as generating ideas to improve them and solve problems. Additionally, the results provided evidence that the engineering design process fosters collaboration and communication. Through iterative testing and modification, the preschoolers exhibited persistence and very positive learning dispositions.

Cepstral Analysis of Voice in School-Aged Children

Dysphonia in school-aged children is attributed primarily to hyperfunctional use of voice. These can be identified through effective protocols using both acoustic and auditory-perceptual analyses. The current study aimed to investigate voice characteristics in school children aged 4-17years using auditory-perceptual rating and cepstral measures of voice. This is a descriptive cross-sectional observational study. Four hundred and fifty-seven recordings of sustained phonation of /a/ in children and adolescents obtained in a quiet room using Zoom h1 voice recorder were analyzed using auditory-perceptual evaluation by three speech-language pathologists using Grade of overall dysphonia, Roughness, Breathiness, Asthenia, and Strain(GRBAS) rating scale. The samples were classified based on age into five groups: 1) 4 to 6; 11 years 2) 7-8; 11 years, 3) 9-11; 11years 4) 12-13; 11years and 5) 14-16; 11 years. PRAAT software was used to extract Cepstral Peak Prominence (CPP) and Cepstral Peak Prominence Smoothed (CPPS). Inter-rater reliability was assessed for both auditory-perceptual and acoustic analysis. Auditory-perceptual analysis revealed dysphonia in 7.8% of samples with higher rate in males than females. Inter-rater reliability for auditory-perceptual rating was found to be good (Intraclass Corelation Coefficient-0.83). Independent t test revealed statistically significant difference (P<0.001) in both cepstral measures and mean values were lower in dysphonic than normal group. Gender effect was present for CPP in group 5(14-16;11 years) and CPPS in group 4 (12-13; 11years). One-way analysis of variance within groups in males (P<0.005) revealed statistical difference in both cepstral measures but not in females. Statistically significant difference was not found between ratings of both speech language pathologists for both CPP (P=0.929) and CPPS (P=0.965) values indicating the ratings to be reliable. Pediatric dysphonia has received less attention when compared to adults. Assessing school-aged children for dysphonia using both auditory-perceptual and acoustic measures would aid in identifying those at risk to make appropriate referrals and plan further intervention.

Do Effects of Voice Rehabilitation in Patients Irradiated for Laryngeal Cancer Remain 5 Years Postradiotherapy?

Laryngeal cancer treated by radiotherapy results in many patients being left with an abnormal voice long-term. This prospective study aims to report efficacy of voice rehabilitation 5years postradiotherapy completion. Seventy-seven patients were randomized into an intervention group (n=37) or a control group (n=40). Voice rehabilitation was administered postradiotherapy. Patients were followed at baseline, 12- and 60-month postradiotherapy with voice recordings assessed using GRBAS protocol (Grade, Roughness, Breathiness, Asthenia, Strain). Patients filled in the European Organization for Research and Treatment of Cancer Quality-of-Life Questionnaire Head and Neck 35 (EORTC QLQ-HN35) and the Swedish Self-Evaluation of Communication Experiences after Laryngeal Cancer (S-SECEL). There were no statistically significant differences between the intervention and control group in scores reported on EORTC QLQ-HN35, S-SECEL or in perceptual evaluation at study end-point. The intervention group reported an improvement in EORTC QLQ-HN35 Speech between baseline-60months postradiotherapy. No significant changes between 12-60months were observed. The control group demonstrated significant improvement in domains Pain, Senses, Speech, Social eating and Sexuality from baseline-60months postradiotherapy, of which only Speech showed a statistically significant change between 12-60months postradiotherapy (P=0.02). Both groups reported improved S-SECEL scores from baseline-60months, with no significant dynamic between the 12- and 60-month follow-up. Previously observed positive effects of voice rehabilitation on patient communicative skills and perceptual evaluation are no longer noticeable at 5-year post voice therapy completion. Nevertheless, patients receiving voice rehabilitation experience a greater improvement within the first year, which in the control group takes a corresponding 5years.

Parkinson’s Disease Detection

Abstract: Parkinson’s Disease is a disorder that affects the nervous system. Parkinson’s disease does not directly cause people to die but can make some people more vulnerable to serious and life-threatening infections. This research addresses the limitations of traditional clinical diagnosis by harnessing the potential of advanced data analysis techniques and machine learning algorithms. The project’s primary objectives include dataset compilation, feature extraction, model development, multimodal fusion, model validation, and considerations for clinical applicability. The dataset will encompass a diverse range of participants diagnosed with PD as well as healthy controls, ensuring the representation of various demographic and clinical factors. By extracting distinctive features from voice recordings, handwriting dynamics, and gait patterns, the project aims to capture unique biomarkers associated with PD. Machine learning models, tailored for each modality, will be developed to classify individuals as PD-positive or PD-negative

Navigating Vocal Challenges: Singing Difficulties in a Carnatic Singer with Clinically Normal Voice.

This study aimed to describe the clinical presentation and therapeutic course of a trained Carnatic singer with perceptually clinically normal voice who presented with singing difficulties. The participant of the study was a 25-year-old male individual who reported to the speech and language diagnostics unit with complaints of reduced vocal range for singing and strained voice while singing. After the routine voice evaluations, the subject was allotted Resonant Voice Therapy. The baseline recordings were compared with the subsequent voice recordings of the subject, on different parameters, along the course of the training. The post-training recordings showed an increase in the singing range of the subject from 9 semitones to 19-20 semitones with modifications in adharashruthi. There were reductions in the perturbation and noise-to-harmonic ratio found in post-training samples. This case study highlights the innovative application of RVT to expand pitch range in a Carnatic singer with normative vocal parameters but encountering singing difficulties. The findings underscore the potential of RVT as a transformative intervention, offering promising avenues for enhancing vocal performance and addressing challenges specific to Carnatic singing techniques.

Individualized Developmental Care Practices of Nurses Working in Neonatal Intensive Care Units: A Qualitative Study

Aim: This study aimed to identify and compare neonatal intensive care nurses who received training in individualized developmental care practices with nurses who did not. Material and Methods: This study was conducted with a qualitative design in the third-level neonatal intensive care units of two tertiary hospitals. Seven nurses from each hospital participated in the study. A semi-structured interview form was used. The interviews were recorded with a voice recorder and then transferred to a computer. Content analysis was used to analyze the interview data. Results: Seven themes, 14 sub-themes, and 78 codes were created for the nurses who received training, and 7 themes, 13 sub-themes, and 30 codes were created for the nurses who did not receive training. Since the themes of the two groups were common, the results of the two groups were compared and discussed. Conclusion: It was found that nurses who received training in individualized developmental care approached the newborn and family more holistically, planned, implemented, and evaluated their care more comprehensively than the nurses who did not receive training.