Voice Recordings Research Articles

Therapeutic Singing and Semi-Occluded Vocal Tract Exercises for Individuals with Parkinson's Disease: A Randomized Controlled Trial of a Single Session Intervention.

Individuals with Parkinson's disease (PD) experience speech and voice-related symptoms that diminish communication and quality of life. Semi-occluded vocal tract (SOVT) exercises are targeted interventions that, when combined with the positive psychosocial benefits of therapeutic group singing (TGS), may affect outcomes. The purpose of this study was to explore the effectiveness of SOVT exercises, specifically straw phonation combined with TGS, to improve voice quality and mood for individuals with PD. We used a true experimental pretest-posttest between-subjects design (i.e., randomized controlled trial) facilitated by a board-certified music therapist. All participants (N = 27) were randomly assigned to one of three groups (a) straw phonation combined with TGS (SP + TGS, n = 10), (b) TGS (n = 10), and (c) speaking-only control group (n = 7). Participants completed voice recordings for acoustic measures and the Visual Analogue Mood Scale for mood analysis before and after a 30-min intervention. The results demonstrated significant improvement in voice quality evidenced by decreasing Acoustic Voice Quality Index scores following a single session for both SP + TGS and TGS intervention groups when compared to the control. Happiness scores improved in the experimental groups when compared to control. Although not statistically significant, participants in the experimental groups (SP + TGS, TGS) demonstrated better mean mood scores on happiness, anxiety, and angry when compared to control, indicating a positive psychological response to the singing interventions. Overall, this study indicated the effectiveness of SP + TGS and TGS as promising therapeutic interventions for voice quality and mood in individuals with PD.

A qualitative exploration of patients’ perception regarding the comprehensive dental services availed at a primary health center

Introduction Comprehensive oral care is a service centered around the patient, and individuals who need it the most often face limited access. Patient perception acts as a guide for enhancing quality, ensuring patients’ future intent to utilize the services and facilitating recommendations to others. The present study aimed to assess the patients’ perception of comprehensive dental services availed at a Primary Health Center (PHC). Methods This qualitative study was based on a phenomenological interpretive approach, and judgment sampling method was employed. A validated interview guide, developed from relevant literature was employed in the local language to conduct interviews among adults visiting the PHC, gathering their views regarding the services provided. The interviews were audio recorded on a digital voice recorder, and files were password protected. Content saturation guided the determination of the final number of participants interviewed. After translating and transcribing the interviews, thematic analysis and coding were performed using ATLAS. ti 23 for Windows. Results A total of 12 participants were included in the study, following data saturation. Among them, there were 8(66.7%) female and 4(33.3%) male participants. Ten overarching main themes were discerned through the assigned codes, including positive views, neutral views, negative views, previous dental clinics visited, previous experience with dental treatment, treatments sought at the center, referrals, source of information about the dental center, subsequent visits and suggestions for improvement. Conclusions The findings of this study revealed a positive patient perception of the comprehensive dental services offered at the PHC. Through insightful interviews, various strengths, and areas for improvement regarding the center and care provision were identified. These insights provide valuable suggestions that can be applied to elevate the utilization of dental services, ensuring continuous improvement in patient care.

Improving Medical Speech-to-Text Accuracy using Vision-Language Pre-training Models.

Automatic Speech Recognition (ASR) is a technology that converts spoken words into text, facilitating interaction between humans and machines. One of the most common applications of ASR is Speech-To-Text (STT) technology, which simplifies user workflows by transcribing spoken words into text. In the medical field, STT has the potential to significantly reduce the workload of clinicians who rely on typists to transcribe their voice recordings. However, developing an STT model for the medical domain is challenging due to the lack of sufficient speech and text datasets. To address this issue, we propose a medical-domain text correction method that modifies the output text of a general STT system using the Vision Language Pre-training (VLP) method. VLP combines textual and visual information to correct text based on image knowledge. Our extensive experiments demonstrate that the proposed method offers quantitatively and clinically significant improvements in STT performance in the medical field. We further show that multi-modal understanding of image and text information outperforms single-modal understanding using only text information.

INTERFERENSI BAHASA MELAYU MALAYSIA DALAM BERBAHASA INDONESIA MANTAN TKI DI DUSUN TANAH EJA KABUPATEN BULUKUMBA

This study aims to: (1) describe the phonological interference form of speech of former migrant workers in Tanah Eja Hamlet, Bulukumba Regency; and (2) describe the form of semantic interference of former migrant workers' speech in Tanah Eja hamlet, Bulukumba Regency. This type of research is qualitative research. This research was conducted in Tanah Eja hamlet, Bulukumba Regency, South Sulawesi. The focus of this research and the limitations of this research include tuturain kaita that experience interference, then studied in the semaintic category that is only related to lexical meaning. As for the phonological category, three types of phonological interference will be focused, namely phonological interference, phoneme changes, phoneme addition, and phoneme reduction / removal interference. The source of this research data was obtained by dairi haisil waiwaincairai laingsung maintain TKI in Tainaih Ejai Kaibupaiten Bulukumba hamlet. Data collection techniques through interview techniques, voice recording techniques, listening techniques and recording techniques. The research instrument consists of the main instrument is the researcher himself and supporting instruments include voice recorders, respondent biodata questionnaires, and interference data tables.The results revealed that from the results of direct interview research 15 respondents contained a total of 35 words phonological interference. The most abundant and frequent data is the category of changes in vowel phonemes in the middle of words. In addition, respondents were shown to experience semantic interference by obtaining 15 forms of conversation and 28 types of words that experienced interference, this conversation was quoted during structured and semi-structured interviews of former migrant workers in Tanah Eja hamlet.

Collaborative System Usability in Spaceflight Analog Environments through Remote Observations

The conventional design cycle in human–computer interaction faces significant challenges when applied to users in isolated settings, such as astronauts in extreme environments. Challenges include obtaining user feedback and effectively tracking human–software/human–human dynamics during system interactions. This study addresses these issues by exploring the potential of remote conversation analysis to validate the usability of collaborative technology, supplemented with a traditional post hoc survey approach. Specifically, we evaluate an integrated timeline software tool used in NASA’s Human Exploration Research Analog. Our findings indicate that voice recordings, which focus on the topical content of intra-crew speech, can serve as non-intrusive metrics for essential dynamics in human–machine interactions. The results emphasize the collaborative nature of the self-scheduling process and suggest that tracking conversations may serve as a viable proxy for assessing workload in remote environments.

Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction

Background: Parkinson's disease (PD) is a critical neurodegenerative disorder affecting the central nervous system and often causing impaired movement and cognitive function in patients. In addition, its diagnosis in the early stages requires a complex and time-consuming process because all existing tests such as electroencephalography or blood examinations lack effectiveness and accuracy. Several studies explored PD prediction using sound, with a specific focus on the development of classification models to enhance accuracy. The majority of these neglected crucial aspects including feature extraction and proper parameter tuning, leading to low accuracy. Objective: This study aims to optimize performance of voice-based PD prediction through feature extraction, with the goal of reducing data dimensions and improving model computational efficiency. Additionally, appropriate parameters will be selected for enhancement of the ability of the model to identify both PD cases and healthy individuals. Methods: The proposed new model applied an OpenML dataset comprising voice recordings from 31 individuals, namely 23 PD patients and 8 healthy participants. The experimental process included the initial use of the SVM algorithm, followed by implementing PCA for feature extraction to enhance machine learning accuracy. Subsequently, data balancing with SMOTE was conducted, and GridSearchCV was used to identify the best parameter combination based on the predicted model characteristics. Result: Evaluation of the proposed model showed an impressive accuracy of 97.44%, sensitivity of 100%, and specificity of 85.71%. This excellent result was achieved with a limited dataset and a 10-fold cross-validation tuning, rendering the model sensitive to the training data. Conclusion: This study successfully enhanced the prediction model accuracy through the SVM+PCA+GridSearchCV+CV method. However, future investigations should consider an appropriate number of folds for a small dataset, explore alternative cross-validation methods, and expand the dataset to enhance model generalizability. Keywords: GridSearchCV, Parkinson Disaese, SVM, PCA, SMOTE, Voice/Speech

Machine Learning-Assisted Speech Analysis for Early Detection of Parkinson's Disease: A Study on Speaker Diarization and Classification Techniques.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. One of the notable non-motor symptoms of PD is the presence of vocal disorders, attributed to the underlying pathophysiological changes in the neural control of the laryngeal and vocal tract musculature. From this perspective, the integration of machine learning (ML) techniques in the analysis of speech signals has significantly contributed to the detection and diagnosis of PD. Particularly, MEL Frequency Cepstral Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GTCCs) are both feature extraction techniques commonly used in the field of speech and audio signal processing that could exhibit great potential for vocal disorder identification. This study presents a novel approach to the early detection of PD through ML applied to speech analysis, leveraging both MFCCs and GTCCs. The recordings contained in the Mobile Device Voice Recordings at King's College London (MDVR-KCL) dataset were used. These recordings were collected from healthy individuals and PD patients while they read a passage and during a spontaneous conversation on the phone. Particularly, the speech data regarding the spontaneous dialogue task were processed through speaker diarization, a technique that partitions an audio stream into homogeneous segments according to speaker identity. The ML applied to MFCCS and GTCCs allowed us to classify PD patients with a test accuracy of 92.3%. This research further demonstrates the potential to employ mobile phones as a non-invasive, cost-effective tool for the early detection of PD, significantly improving patient prognosis and quality of life.

A digital teleneuropsychology platform for the diagnosis of mild cognitive impairment: from concept to certification as a medical device.

Innovative digital solutions are shaping a new concept of dementia care, opening additional venues for prevention, diagnosis, monitoring and treatment. Hereby, we report the development of a tablet-based teleneuropsychology platform (Tenèpsia®), from concept to certification as Medical Device (MD) Class IIA, as per new MD regulation 745/2017. The platform was designed for the remote cognitive evaluation and created thanks to the effort of a collaborative working group including experts from three Italian scientific societies and Biogen Italia S.r.l. (hereafter "Biogen"), and developers from Xenia Reply and Inside AI. The development strategy was guided by converting traditional paper-and-pencil tests into digital versions while maintaining comparable neuropsychological features and optimizing patient accessibility and user experience. The experts focused on the choice and adaptation of traditional neuropsychology measures for a 45-min teleneuropsychology assessment. The developers created a web and a mobile interface, respectively, for the professional (neuropsychologist) and non-professional (patient and caregiver) use. Recording of voice, drawing and typing information was enabled. Instant dashboards provide a quick overview of the patient's condition. Simulation activities were performed to obtain MD certification, valid across Europe. Neuropsychology services will benefit from the implementation in clinics of harmonized digital tools with adequate scientific and technological standards. The use of digital cognitive testing for the diagnosis of mild cognitive impairment is expected to enhance patient and clinician outcomes through simplified, digital objective data collection, sparing of time and resources, with a positive impact on healthcare costs and access to treatments, reducing inequalities and delays in diagnosis and cure.

ANALYSIS OF ORAL TRADITION AS A HISTORICAL SOURCE

This study aims to investigate the role of oral tradition as a significant source in reconstructing history. The research method used is a literature study, with reference to several previous studies that mainly focused on collecting data from libraries and previous research. The results of this study are intended to provide an overview and description of the facts, nature, and relationships between phenomena that occur. The use of oral tradition as a historical source provides a valuable alternative, although there are indeed weaknesses and limitations in its verification capacity. Oral tradition is not only limited to the oral dimension, but also includes forms and patterns of oral communication. It became an integral part of the culture and had links to local history. One strategy to utilize oral tradition as a source of local history is to record it through interview techniques and document it in the form of sound or written recordings. Such interview notes and voice recordings can be integrated as part of a document that can be analyzed with the support of other reference materials. The importance of oral tradition in local history is not only limited to cultural value, but also can create economic value for the community in this era.

To trust or not to trust? Face and voice modulation of virtual avatars

Introduction: This study explores the graduated perception of apparent social traits in virtual characters by experimental manipulation of perceived affiliation with the aim to validate an existing predictive model in animated whole-body avatars.Methods: We created a set of 210 animated virtual characters, for which facial features were generated according to a predictive statistical model originally developed for 2D faces. In a first online study, participants (N = 34) rated mute video clips of the characters on the dimensions of trustworthiness, valence, and arousal. In a second study (N = 49), vocal expressions were added to the avatars, with voice recordings manipulated on the dimension of trustworthiness by their speakers.Results: In study one, as predicted, we found a significant positive linear (p < 0.001) as well as quadratic (p < 0.001) trend in trustworthiness ratings. We found a significant negative correlation between mean trustworthiness and arousal (τ = −.37, p < 0.001), and a positive correlation with valence (τ = 0.88, p < 0.001). In study two, wefound a significant linear (p < 0.001), quadratic (p < 0.001), cubic (p < 0.001), quartic (p < 0.001) and quintic (p = 0.001) trend in trustworthiness ratings. Similarly, to study one, we found a significant negative correlation between mean trustworthiness and arousal (τ = −0.42, p < 0.001) and a positive correlation with valence (τ = 0.76, p < 0.001).Discussion: We successfully showed that a multisensory graduation of apparent social traits, originally developed for 2D stimuli, can be applied to virtually animated characters, to create a battery of animated virtual humanoid male characters. These virtual avatars have a higher ecological validity in comparison to their 2D counterparts and allow for a targeted experimental manipulation of perceived trustworthiness. The stimuli could be used for social cognition research in neurotypical and psychiatric populations.

Exploring the impact of type II diabetes mellitus on voice quality.

This cross-sectional study aimed to investigate the potential of voice analysis as a prescreening tool for type II diabetes mellitus (T2DM) by examining the differences in voice recordings between non-diabetic and T2DM participants. 60 participants diagnosed as non-diabetic (n = 30) or T2DM (n = 30) were recruited on the basis of specific inclusion and exclusion criteria in Iran between February 2020 and September 2023. Participants were matched according to their year of birth and then placed into six age categories. Using the WhatsApp application, participants recorded the translated versions of speech elicitation tasks. Seven acoustic features [fundamental frequency, jitter, shimmer, harmonic-to-noise ratio (HNR), cepstral peak prominence (CPP), voice onset time (VOT), and formant (F1-F2)] were extracted from each recording and analyzed using Praat software. Data was analyzed with Kolmogorov-Smirnov, two-way ANOVA, post hoc Tukey, binary logistic regression, and student t tests. The comparison between groups showed significant differences in fundamental frequency, jitter, shimmer, CPP, and HNR (p < 0.05), while there were no significant differences in formant and VOT (p > 0.05). Binary logistic regression showed that shimmer was the most significant predictor of the disease group. There was also a significant difference between diabetes status and age, in the case of CPP. Participants with type II diabetes exhibited significant vocal variations compared to non-diabetic controls.

LFC study: Protocol for a longitudinal follow-up cohort study on ageing and mental health in community-dwelling older adults in Singapore

The rapid pace of population ageing worldwide has prompted the need to better understand the ageing process. The current study, titled the Longitudinal Follow-up of the CHI (LFC) study, was a 3-year follow-up study of an earlier study titled the Community Health and Intergenerational (CHI) study. The LFC study looked to examine longitudinal changes in their cognitive functioning and psychosocial outcomes across the 3-year period. Additionally, the current study built upon the earlier CHI study by collecting neuroimaging data and exploring the long-term effects of non-pharmacological interventions, which were not examined in the prior study. A total of 653 community-dwelling participants from the baseline CHI study cohort were invited to take part in the LFC study, where they underwent a battery of neuropsychological assessments, psychosocial questionnaires, a Magnetic Resonance Imaging scan and a voice recording segment. The current study would holistically track longitudinal changes in cognitive functioning and psychosocial outcomes in the ageing population in Singapore. Unique associations between linguistics and neuroimaging data alongside cognitive and psychosocial outcomes would be explored. This study also serves to guide the development of new interventions for older adults and assist in improving the well-being of the local and global ageing population.

Nurses' experiences of using a computer-based triage decision support system in the emergency department.

Emergency department triage is a vital and complex decision-making process. There is limited knowledge about nurses' experiences with triage decision support systems in emergency departments. This study was conducted to examine nurses' experiences with a computer-based triage decision support system in the emergency department. This is a qualitative and phenomenological study. Data were collected through interviews from 14 triage nurses who used a triage decision support system in the emergency department of a university hospital. The nurses were recruited for semi-structured interviews. All interviews were recorded on a voice recorder and then transcribed. Data were analysed with the inductive content analysis method. The interviewer asked comprehensive questions about the nurses' experiences with the triage decision support system. Three main and 11 sub-themes were elicited as a result of the analysis of the in-depth interviews: (a) the facilitating the triage decision theme, which included help in case of a dilemma, team collaboration, monitoring/supervision, and error reduction sub-themes; (b) the contribution to professionalism theme, which included ease of learning and teaching triage, professional autonomy, creating a database, and evidence-based practice sub-themes; (c) the areas that need improvement theme, which included reducing screen clicks, the effect of the hospital automation system performance, and clinical descriptors not included in the algorithm sub-themes. Triage nurses stated that the decision support system was beneficial and facilitated decision-making. The decision support system enabled triage nurses to make their own decisions using their clinical knowledge and experience, without a restriction on their professional autonomy, and this was perceived positively. It was emphasized that this system could be a support tool in educating nurses new to triage. However, nurses stated that the pace of the decision support system was affected by the performance of the hospital automation system and that the large number of steps used for electing items caused a waste of time. They also added that these were technical areas that needed improvement. The study provides important data that will help healthcare organizations and professionals better understand the emergency department nurse triage decision support system and gain a versatile, comprehensive, and general understanding.

Children and extensive reading motivation

Abstract Extensive reading (ER), which refers to reading a lot of often self-selected material for information, overall meaning, pleasure, and enjoyment, has been growing in popularity in Asia as an effective way for English as a foreign language (EFL) students to improve reading skills and has provided opportunities for comprehensible input (Nation &amp; Waring, 2013). The focus of this action research (AR) project is on young EFL learners in a private language school setting without a grading system. Therefore, it is important that students be intrinsically motivated to read for the school’s ER program to be successful, specifically, the motivation to check out and read books from a school library. Furthermore, this AR project sought to find whether certain methods can increase students’ intrinsic motivation to read. The research methods include questionnaires, student voice recordings, and empirical data on book-borrowing behaviors. The findings show that indirect, rather than direct, teacher recommendation of books seems to be the most effective method to increase intrinsic motivation to do ER.

Unveiling the sound of the cognitive status: Machine Learning-based speech analysis in the Alzheimer's disease spectrum.

Advancement in screening tools accessible to the general population for the early detection of Alzheimer's disease (AD) and prediction of its progression is essential for achieving timely therapeutic interventions and conducting decentralized clinical trials. This study delves into the application of Machine Learning (ML) techniques by leveraging paralinguistic features extracted directly from a brief spontaneous speech (SS) protocol. We aimed to explore the capability of ML techniques to discriminate between different degrees of cognitive impairment based on SS. Furthermore, for the first time, this study investigates the relationship between paralinguistic features from SS and cognitive function within the AD spectrum. Physical-acoustic features were extracted from voice recordings of patients evaluated in a memory unit who underwent a SS protocol. We implemented several ML models evaluated via cross-validation to identify individuals without cognitive impairment (subjective cognitive decline, SCD), with mild cognitive impairment (MCI), and with dementia due to AD (ADD). In addition, we established models capable of predicting cognitive domain performance based on a comprehensive neuropsychological battery from Fundació Ace (NBACE) using SS-derived information. The results of this study showed that, based on a paralinguistic analysis of sound, it is possible to identify individuals with ADD (F1 = 0.92) and MCI (F1 = 0.84). Furthermore, our models, based on physical acoustic information, exhibited correlations greater than 0.5 for predicting the cognitive domains of attention, memory, executive functions, language, and visuospatial ability. In this study, we show the potential of a brief and cost-effective SS protocol in distinguishing between different degrees of cognitive impairment and forecasting performance in cognitive domains commonly affected within the AD spectrum. Our results demonstrate a high correspondence with protocols traditionally used to assess cognitive function. Overall, it opens up novel prospects for developing screening tools and remote disease monitoring.

Doctors' perspectives on the quality of medical imaging in public hospitals in eThekwini District.

There is a paucity of literature on perspectives of referring doctors about the quality of medical imaging services and this study closes this gap in literature. This quality assurance (QA) study aimed to explore the perspectives of doctors on the quality of medical imaging services in selected regional hospitals within eThekwini District of KwaZulu-Natal. The study was conducted in four public regional hospitals. An exploratory descriptive qualitative research design involving 30 min-45 min of in-depth individual interviews was used. A purposive sampling technique was used to select research participants and hospitals to ensure adequate responses to the research questions. The sample involved nine participants and was guided by data saturation. Responses were recorded through notes and voice recordings and thematic analysis was used to analyse data. Three main themes (timeliness of examinations, communication and radiology reports and image quality) and eight subthemes (waiting times, shortage of radiographers, workload, communication between doctors and radiographers, requisition forms, unavailability of radiology reports, clarity of images and image acquisition protocols) emerged from the data. Challenges experienced were exacerbated by high workload and shortage of radiologists and radiographers. Doctors in the data collection sites were mainly dissatisfied with services provided by the medical imaging departments. Regular engagements between medical imaging departments and doctors are important in enhancing the provision of quality care to patients. In-service training of radiographers and employment of additional radiographers and finding solutions to mitigate shortage of radiologists are recommended. This quality assurance (QA) study focused on experiences of doctors while many other medical imaging QA studies in South Africa are equipment based. In-service training of radiographers is recommended to improve image quality and communication skills.

ML-Driven Facial Synthesis from Spoken Words Using Conditional GANs

A Human Brain may translate a person's voice to its corresponding face image even if never seen before. Training adeep learning network to do the same can be used in detecting human faces based on their voice, which may be used in findinga criminal that we only have a voice recording for. The goal in this paper is to build a Conditional Generative Adversarial Network that produces face images from human speeches which can then be recognized by a face recognition model to identifythe owner of the speech. The model was trained, and the face recognition model gave an accuracy of 80.08% in training and 56.2% in testing. Compared to the basic GAN model, this model has improved the results by about 30%. Key Word: Face image synthesis, Generative adversarial network, Face Recognition

The effect of voice recording as a tool to improve speaking skills

This research studies the effects of voice recording technology as an educational tool to enhance speaking skills with a group of 15 students of the first bachelorette at a public high school in Paute, Ecuador. The methodology applied a mixed-methods approach in which a pre and post-test were applied to gather quantitative data and a questionnaire with eight open-ended questions to collect qualitative data and analyze participants’ perceptions. During the intervention between the tests, students had to follow some steps to complete voice recording activities. Learners planned the speaking part, practiced the main components like fluency, pronunciation and intonation, vocabulary and grammar, and content and organization, then recorded the audio, and finally uploaded it to the WhatsApp group. Learners received guidance, feedback, and personalized instruction based on their recorded performances. Moreover, after the study, students perceived the voice recording activities as a significant benefit and considered this tool as an opportunity to improve their speaking skills. making the learning process more meaningful. Results revealed that voice recording provides a practical and effective tool for the development and continuous improvement of communication skills.

Machine Learning-Based Classification of Parkinson's Disease Patients Using Speech Biomarkers.

Parkinson's disease (PD) is the most prevalent neurodegenerative movement disorder and a growing health concern in demographically aging societies. The prevalence of PD among individuals over the age of 60 and 80 years has been reported to range between 1% and 4%. A timely diagnosis of PD is desirable, even though it poses challenges to medical systems. This study aimed to classify PD and healthy controls based on the analysis of voice records at different frequencies using machine learning (ML) algorithms. The voices of 252 individuals aged 33 to 87 years were recorded. Based on the voice record data, ML algorithms can distinguish PD patients and healthy controls. One binary decision variable was associated with 756 instances and 754 attributes. Voice records data were analyzed through supervised ML algorithms and pipelines. A 10-fold cross-validation method was used to validate models. In the classification of PD patients, ML models were performed with 84.21 accuracy, 93 precision, 89 Sensitivity, 89 F1-scores, and 87 AUC. The pipeline performance improved to accuracy: 85.09, precision: 92, Sensitivity:91, F1-score: 89, and AUC: 90. The Pipeline methods improved the performance of classifying PD from voice record. Our study demonstrated that ML classifiers and pipelines can classify PD patients based on speech biomarkers. It was found that pipelines were more effective at selecting the most relevant features from high-dimensional data and at accurately classifying PD patients and healthy controls. This approach can therefore be used for early diagnosis of initial forms of PD.

Artificial intelligence for predicting orthodontic patient cooperation: Voice records versus frontal photographs

Objectives: The purpose of this study was to compare the predictive ability of different convolutional neural network (CNN) models and machine learning algorithms trained with frontal photographs and voice recordings. Material and Methods: Two hundred and thirty-seven orthodontic patients (147 women, 90 men, mean age 14.94 ± 2.4 years) were included in the study. According to the orthodontic patient cooperation scale, patients were classified into two groups at the 12th month of treatment: Cooperative and non-cooperative. Afterward, frontal photographs and text-to-speech voice records of the participants were collected. CNN models and machine learning algorithms were employed to categorize the data into cooperative and non-cooperative groups. Nine different CNN models were employed to analyze images, while one CNN model and 13 machine learning models were utilized to analyze audio data. The accuracy, precision, recall, and F1-score values of these models were assessed. Results: Xception (66%) and DenseNet121 (66%) were the two most effective CNN models in evaluating photographs. The model with the lowest success rate was ResNet101V2 (48.0%). The success rates of the other five models were similar. In the assessment of audio data, the most successful models were YAMNet, linear discriminant analysis, K-nearest neighbors, support vector machine, extra tree classifier, and stacking classifier (%58.7). The algorithm with the lowest success rate was the decision tree classifier (41.3%). Conclusion: Some of the CNN models trained with photographs were successful in predicting cooperation, but voice data were not as useful as photographs in predicting cooperation.