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503
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ShareScore release 0.9.0
Dataset results
503 results for “Voice”
VoIce Changes and Heart Failure
ClinicalTrials.gov study NCT02555904. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Longitudinal Non-invasive Analysis of VOCAle Cord Function Based on Trans-laryngeal Ultrasound Acquisitions and Voice Recordings
ClinicalTrials.gov study NCT06776393. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Efficacy Study of an Audio Device Outside Incubator Allowing Broadcasting Maternal Voice on Stability in Preterm Infants
ClinicalTrials.gov study NCT05543005. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Immediate Effects of Postural Repositioning on Voice Production Tasks in Seated Individuals With Acquired Dysarthria:
ClinicalTrials.gov study NCT04520828. IPD Sharing: Not stated. Countries: 0. Publications: 0.
The Influence of Significant Weight Lost on the Quality of Voice
ClinicalTrials.gov study NCT03070808. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Effect of Maternal Voice and Music Therapy on Pain and Physiological Parameters During in Intubated Children
ClinicalTrials.gov study NCT06230211. IPD Sharing: NO. Countries: 0. Publications: 0.
Evaluation of Voice in Behcet's
ClinicalTrials.gov study NCT06836817. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Voice and Swallowing Disorders in Adults With and Without Alzheimer's Disease
ClinicalTrials.gov study NCT06328374. IPD Sharing: NO. Countries: 0. Publications: 0.
A Comparison of Voice Amplifiers and Personal Communication Systems in Individuals With Parkinson's Disease
ClinicalTrials.gov study NCT02407067. IPD Sharing: NO. Countries: 0. Publications: 0.
Voice Acoustic Properties in People With Obstructive Sleep Apnea Syndrome (OSAS)
ClinicalTrials.gov study NCT03568240. IPD Sharing: NO. Countries: 0. Publications: 0.
Female Teacher's Voice During Teaching
ClinicalTrials.gov study NCT03382535. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Digital Measurements of Motor and Voice Functions in FTD
ClinicalTrials.gov study NCT07333898. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Detection of Atrial Fibrillation Through Voice Signal Processing
ClinicalTrials.gov study NCT07212816. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Computerized automated text and voice analysis - a single case study of seven chronically schizophrenia patients in art therapy (livopict)
<p>This is an explorative monocentric art therapy study in the form of a quantitative single-case study (n-of-one-trial without baseline) in seven patients with chronic schizophrenia. The interventions consist of therapist-guided interviews with patients about their images, which are digitally recorded and analyzed using both text and voice analysis software.</p> <p>This dual study approach of digitized interviews allows correlations between the two portions of the recorded voice samples and forms the internal control in the study approach.</p> <p>The sample of seven patients with chronic schizophrenia (n=7), defined under inclusion and exclusion criteria, engaged in pictorial activity as part of their weekly group art therapy (non- invasive portion of the studies intervention: image creation, IC). In a one-on-one interview after three or four days, study participants were invited to talk with the author about the paintings they had created. An interview developed especially for the study served as a guide and, above all, as a standardized stimulus for the patients' statements (therapist-guided picture discussion, TGPD).</p> <p> </p> <p> </p>
singing voice detection dataset ALL-Pub-SVD-In-One
<p>singing voice detection datasets:</p> <p>it contains the public dataset of Jamendo Dataset, and the RWC dataset , and the iKala dataset, and the MIR-1K dataset , and teh MedlyDB dataset.</p> <p>Jamendo are same with the raw.</p> <p>RWC use the simply mode same with the lable in Jamendo just sing or nosing.</p> <p>iKala and MIR-1K are full wavfiles with the generated lables from the PitchLables. 0 for nosing and other for sing.</p> <p>MedlyDB use part wavfiles from the RAW dataset and also generated lables from the MELODY_2 lables with 0 for nosing and other for nosing.</p> <p> </p>
Singing Voice Audio Dataset
<p>This dataset is for the purpose of the analysis of singing voice. It is our hope that the publication of this dataset will encourage further work into the area of singing voice audio analysis by removing one of the main impediments in this research area - the lack of data (unaccompanied singing).</p> <p>It contains over 70 original vocal recordings by 28 professional, semi-professional and amateur singers. Singing style is predominantly Chinese Opera but some recordings are Western Opera. All recordings are 44.1 KHz sample rate and have been amplitude normalised.</p>
VoiceWukong: Benchmarking Deepfake Voice Detection (part_ab)
<h1>VoiceWukong</h1> <p><em><strong>VoiceWukong </strong>is a comprehensive benchmark for deepfake voice detection, designed to evaluate the performance of various detectors in real-world application scenarios.</em></p> <p><strong>Dataset Features</strong></p> <ul> <li>Large Scale: Contains 265,200 English and 148,200 Chinese deepfake voice samples</li> <li>Diverse Sources: Covers voice samples generated by 19 commercial tools and 15 open-source tools</li> <li>Real-world Scenarios: Constructed 38 data variants covering 6 types of audio manipulations common in practical applications</li> <li>Bilingual Support: Supports evaluation in both Chinese and English languages</li> </ul> <p><strong>Evaluation Results</strong></p> <ul> <li>Conducted comprehensive evaluations on 12 state-of-the-art deepfake voice detectors</li> <li><a href="https://github.com/TakHemlata/SSL_Anti-spoofing">AASIST2</a> achieved the best performance with an Equal Error Rate (EER) of 13.50%</li> <li>Other detectors showed EERs exceeding 20%</li> <li>Results indicate significant challenges for current detectors in practical applications</li> </ul> <p><strong>Human-Machine Comparison Study</strong></p> <ul> <li>Conducted user studies with over 300 participants</li> <li>Comparative analysis of detection capabilities among humans, detectors, and multimodal large language models (<a href="https://github.com/QwenLM/Qwen2-Audio">Qwen2-Audio</a>)</li> <li>Different detectors and humans showed varying identification capabilities for deepfake voices at different deception levels</li> <li>Multimodal large language models demonstrated no effective detection ability</li> </ul> <h2>Dataset</h2> <p>This is the second part of the dataset, and it requires the complete download of both <a href="https://zenodo.org/records/13731918"><strong><em>part_aa</em></strong></a> and <a href="https://zenodo.org/records/13732412"><strong><em>part_ab</em></strong></a> for proper extraction and use. Please ensure that both files are in the same folder. For a detailed introduction to the data, please refer to our paper (to be made available).</p> <p>The first part (part_aa) is at <a href="../records/13731918">part_aa</a><br>extract command : <code>cat VoiceWukong.part_* | tar -xz</code></p> <h2>Leaderboard</h2> <p>Our leaderboard presents comprehensive evaluation results in three main sections:</p> <ol> <li><strong>Overall Performance</strong> - General evaluation metrics for each detector across the entire dataset, providing a broad view of detection capabilities.</li> <li><strong>Manipulation-specific Performance</strong> - Detailed results showing how each detector performs under different types of audio manipulations, offering insights into specific strengths and weaknesses.</li> <li><strong>User Study-based Evaluation</strong> - Performance analysis of detectors on deepfake voices categorized by difficulty levels based on our user study results, demonstrating detector effectiveness across varying deception capabilities.</li> </ol> <p>Visit our <a href="https://voicewukong.github.io/">leaderboard(github.io)</a> for detailed performance metrics and rankings. Additionally, we provide a copy of the leaderboard code <a href="https://zenodo.org/uploads/14650793">here</a> for permanent storage.</p> <div> <h2>Evaluated Detectors' Weighted Models</h2> <ul> <li>All evaluated detectors’ weighted models can be obtained from <a title="https://huggingface.co/VoiceWukong/VoiceWukong/" href="https://huggingface.co/VoiceWukong/VoiceWukong/">huggingface.co</a>. Additionally, we provide a copy of the weights files <a href="https://zenodo.org/uploads/14650793">here</a> for premanent storage.</li> </ul> <h2>User Study Results & Original Outputs</h2> <ul> <li>This <a href="https://github.com/VoiceWukong/VoiceWukong">code repository(github)</a> stores our <a href="https://github.com/VoiceWukong/VoiceWukong/tree/main/Userstudy/result">user study results</a> and the <a href="https://github.com/VoiceWukong/VoiceWukong/tree/main/OutputScore">original outputs</a> of the evaluation detectors. Additionally, we provide a copy of the code repository <a href="https://zenodo.org/uploads/14650793">here</a> for permanent storage.</li> </ul> </div> <p>Note: <em><strong>VoiceWukong</strong></em> <strong>prohibits use for <em>commercial purposes.</em></strong></p>
Dataset related to: Voice alterations, dysarthria and respiratory derangements in patients with Parkinson's disease
<p>Dataset related to the article:</p> <p>Di Pietro DA, Olivares A, Comini L, Vezzadini G, Luisa A, Petrolati A, Boccola S, Boccali E, Pasotti M, Danna L, Vitacca M. Voice Alterations, Dysarthria, and Respiratory Derangements in Patients With Parkinson's Disease. J Speech Lang Hear Res. 2022 17;65(10):3749-3757.</p> <p>Purpose: Almost 90% of people with Parkinson’s disease (PD) develop voice and speech disorders during the course of the disease. Ventilatory dysfunction is one of the main causes. We aimed to evaluate relationships between respiratory impairments and speech/voice changes in PD.<br> Method: At Day 15 from admission, in consecutive clinically stable PD patients in a neurorehabilitation unit, we collected clinical data as follows: comorbidities, PD severity, motor function and balance, respiratory function at rest (including muscle strength and cough ability), during exercise-induced desaturation and at night, voice function (Voice Handicap Index [VHI] and acoustic analysis [Praat]),<br> speech disorders (Robertson Dysarthria Profile [RDP]), and postural abnormalities. Based on an arbitrary RDP cutoff, two groups with different dysarthria degree were identified—moderate–severe versus no–mild dysarthria—and compared.<br> Results: Of 55 patients analyzed (median value Unified Parkinson’s Disease Rating Scale Part II 9 and Part III 17), we found significant impairments in inspiratory and expiratory muscle pressure (> 90%, both), exercise tolerance at 6-min walking distance (96%), nocturnal (12.7%) and exercise-induced (21.8%) desaturation, VHI (34%), and Praat Shimmer% (89%). Patients with moderate–severe dysarthria (16% of total sample) had more comorbidities/disabilities and worse respiratory pattern and postural abnormalities (camptocormia) than those with no–mild dysarthria. Moreover, the risk of presenting nocturnal desaturation,<br> reduced peak expiratory flow, and cough ability was about 11, 13, and 8 times higher in the moderate–severe group.<br> Conclusions: Dysarthria and respiratory dysfunction are closely associated in PD patients, particularly nocturnal desaturation and reduced cough ability. In addition, postural condition could be at the base of both respiratory and voice impairments.</p>
Dataset related to: "Perceptual and qualitative voice alterations detected by GIRBAS in patients with Parkinson's disease: is there a relation with lung function and oxygenation?"
<p>We provide the raw data used for the following article:</p> <p>Olivares A, Comini L, Di Pietro DA, Vezzadini G, Luisa A, Boccali E, Boccola S, Vitacca M. <strong>Perceptual and qualitative voice alterations detected by GIRBAS in patients with Parkinson's disease: is there a relation with lung function and oxygenation?</strong> Aging Clin Exp Res. 2023 Mar;35(3):633-638. doi: 10.1007/s40520-022-02324-4. </p> <p>Abstract</p> <p><strong>Background: </strong>Impairments in respiration, voice and speech are common in people with Parkinson's disease (PD).</p> <p><strong>Aims: </strong>To evaluate the prevalence of dysphonia, assessed by a specific acoustic evaluation and description of the voice by the speech therapist (GIRBAS), and its relation with lung function and oxygenation, in particular cough ability and during the night or exercise desaturation.</p> <p><strong>Methods: </strong>This is a posthoc analysis of a prospective cross-sectional observational study on PD patients collecting anthropometric and clinical data, comorbidities, PD severity, motor function and balance, respiratory function at rest, during exercise and at night, voice function with acoustic analysis and presence of speech disorders, in addition to the GIRBAS scale. Based on GIRBAS Global dysphonia ('G') score, we divided patients into dysphonic (moderate-to-severe deviance from the euphonic condition) vs. no/mild dysphonic and analyzed the relations with respiratory impairments.</p> <p><strong>Results: </strong>We analyzed 55 patients and found significant impairments in both respiratory and voice/speech functions. Most patients (85.5%) presented mild-to-severe deviance from the euphonic condition in at least one GIRBAS perceptual element (80% of cases for Global dysphonia) and only 14.5% did not show deviance in all elements simultaneously. At Odds Ratio analysis, the risk of presenting nocturnal desaturation and reduced peak cough expiratory flow was approximately 24 and 8 times higher, respectively, in dysphonic patients vs. those with no/mild dysphonia.</p> <p><strong>Conclusion: </strong>Perceptual and qualitative evaluation of the voice with GIRBAS showed that mild-to-severe dysphonia was highly prevalent in PD patients, and associated with nocturnal oxygen desaturation and poor cough ability.</p>
iCarB-Voice
<h3><strong>Description</strong></h3> <p>Contains voice samples (.wav files) from 200 data subjects, acquired using two different microphones:</p> <ol> <li>ME72 from Valeo.</li> <li>AT2020 from Audio-Technica.</li> </ol> <p>During the recording, the data subjects were seated in the driver's seat of a vehicle, with the ME72 microphone positioned in the roof of the car to the right of the subject's head and the AT2020 microphone positioned on the dashboard above the steering wheel. </p> <p>The voice samples were acquired in the following scenarios, the aim of which was to incorporate different variabilities into the recorded voice data:</p> <p>(1) <strong>Indoors</strong>: The car was parked inside a garage, and various background noises were deliberately simulated. The data subjects were asked to read a series of 30 sentences in English (or in another language if they were unable to speak English).</p> <ul> <li>Noiseless: Car doors and windows were closed, no deliberate background noises (30 sentences).</li> <li>External noises: Traffic noises (e.g., car horns and sounds of passing cars) were played on a smartphone and emanated through speakers placed near the driver's door, while the driver's window was closed (5 sentences) then open (5 sentences).</li> <li>Internal noises: Different noises were played on a smartphone inside the car, including classical music (5 sentences), rock music (5 sentences), background discussion (5 sentences), and impulsive noises like dogs barking and babies crying (5 sentences).</li> </ul> <p>(2) <strong>Outdoors</strong>: The car was parked outside, and there was no noise simulation (i.e., any background noises were natural). The data subjects were asked to read or make up a series of 30 sentences in a language other than English (unless English was their only language).</p> <ul> <li>Window closed: The driver's window was closed (15 sentences).</li> <li>Window open: The driver's window was open (15 sentences).</li> </ul> <p><br>So in total, this dataset consists of 180 voice samples per data subject. This amounts to 36,000 voice samples for all 200 data subjects.</p> <p> </p> <h3><strong>Reference</strong></h3> <p>If you use this dataset, please cite the following publication:</p> <p>V. Krivokuca Hahn, J. Maceiras, A. Komaty, P. Abbet and S. Marcel, 2024. "<em>in-Car Biometrics (iCarB) Datasets for Driver Recognition: Face, Fingerprint, and Voice</em><span>". arXiv:2411.17305, doi: <a href="https://doi.org/10.48550/arXiv.2411.17305" target="_blank" rel="noopener">https://doi.org/10.48550/arXiv.2411.17305</a>.</span></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.