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503 results for “Voice”
Voices from Cabinda: Audio files of the examples in the chapter of "Afro-Iberian Languages: Contact and Sociohistory" (LangSciPress)
<p>Audio files (and a few videos) corresponding to the examples presented throughout the chapter on Cabindan Portuguese, included in the volume <em>Afro-Iberian Languages: Contact and Sociohistory</em> (Lamberti/Agostinho, eds.; Language Science Press: https://langsci-press.org/catalog/book/392). The chapter provides a panoramic overview of the main structural features of Cabindan Portuguese, preceded by a historical section on the spread of Portuguese in this Central African region, as well as a sociolinguistic section.</p>
CAVEMOVE: An acoustic database for the study of voice-enabled technologies inside moving vehicles
<p>We provide a collection of multichannel audio recordings obtained inside four different cars. The recording process involves (i) recordings of acoustic impulse responses, which are acquired at static conditions and provide the means for modeling the speech and car-audio components (ii) recordings of acoustic noise at a wide range of both static and in-motion conditions. Data is recorded with two different microphone configurations and particularly (i) a compact microphone array or (ii) a distributed microphone setup. A <a href="https://github.com/SPL-FORTH-ICS/CAVEMOVE">python API and a Matlab API</a>, that can be freely downloaded from CAVEMOVE github, is provided as the means to easily exploit the open access audio recordings to synthesize mixtures of speech and acoustic noise at different driving conditions. This way, the user can easily synthesize the microphone signals required for research on voice enabled technologies inside moving vehicles.</p> <p>All audio recordings and impulse responses are provided at 16 kHz sampling rate and are in the form of 8-channel .wav files.</p> <p>Some basic principles followed in CAVEMOVE APIs are the following. <br>- We provide functions for retrieving speech and noise components as separate entities (e.g. numpy arrays). Users must then add the speech and noise components to derive a mixture.<br>- Noise recordings are derived as a function of driving conditions, specifically the speed (in km/hour) and the window aperture (3 or 4 different windows conditions are considered in each vehicle)<br>-Apart from the basic noise components, we also provide means for adding ventilation/air-condition noise and also, interference from the built-in car audio system (e.g. radio, cd player etc)<br>-To produce the speech components, users must provide their own dry speech recordings <br>-To produce the car-audio components, users must provide their own audio signals.</p> <p>The Documentation .pdf file that we provide along with the audio recordings lists all the conditions that were recorded or measured inside the three cars (the same documantation can also be found in CAVEMOVE github). This information is important for correct use of the python API, since, asking for a condition that was not recorded can potentially produce an error. Note that a list of recorded driving conditions given the car name and the microphone configuration can also be retrieved from auxiliary functions included in the APIs.</p> <p>For any questions with respect to CAVEMOVE dataset or API, feel free to send an email to <br>Andreas Symiakakis at andrysmi@ics.forth.gr <br>or<br>Nikos Stefanakis at nstefana@ics.forth.gr</p> <p>CAVEMOVE project is funded by the Institute of Computer Science of the Foundation for Research and Technology-Hellas (FORTH).</p>
Players' decisions data for exit-voice irrigation experiment
<p>This data contains the decisions data of human-subject players who participated in the irrigation exit-voice experiment. </p>
Towards a simultaneously speaking bilingual robot: Primary study on the effect of gender and pitch of the robot's voice
<p>With fast and reliable international transportation, more people with different language backgrounds can interact now. As a result, the need for communicative agents fluent in several languages to assist those people is highlighted. The high cost of hiring human attendants fluent in several languages makes using social robots a more affordable alternative in international gatherings. A social robot capable of presenting a piece of information in more than one language at the same time to its audience is the goal of this line of study. However, the negative effect of background noise on speech comprehension in humans is well-established. Hence, presenting a piece of information in two different languages at the same time by the robot creates an adverse listening condition for both individuals listening to the speech of such a bilingual robot. In this study, we investigated whether manipulating the pitch and gender of the robot's voice could affect human subjects' memory of the presented information in the presence of background noise. The results indicate that the pitch and gender of the speaking voice do indeed affect our memory of the presented information. when a male voice was used, a higher pitch resulted in significantly better memory performance than a lower pitch. Contrarily, when a female voice was used, a lower pitch resulted in significantly better memory in participants than a higher pitch. Both male and female subjects performed significantly better with a female voice in a noisy background. In nutshell, the result of this study suggests using a female voice for robots in noisy conditions, as in the case of simultaneously speaking robots, can significantly improve the retrieval of presented information in human subjects.</p>
Latin-American voice anti-spoofing dataset
<p>This dataset contains samples of spoof and real human voice with different accents from Latin-American countries.</p> <p> Table 1. Real samples distribution</p> <table> <tbody> <tr> <td><strong>Accent</strong></td> <td><strong>Gender</strong></td> <td><strong># Speakers</strong></td> <td><strong># Files</strong></td> <td><strong>Nomenclature</strong></td> </tr> <tr> <td>Colombian</td> <td> <table> <tbody> <tr> <td>Male</td> </tr> <tr> <td>Female</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>17</td> </tr> <tr> <td>14</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>2534</td> </tr> <tr> <td>2070</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>com</td> </tr> <tr> <td>cof</td> </tr> </tbody> </table> </td> </tr> <tr> <td>Chilean</td> <td> <table> <tbody> <tr> <td>Male</td> </tr> <tr> <td>Female</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>17</td> </tr> <tr> <td>12</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>2487</td> </tr> <tr> <td>1602</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>clm</td> </tr> <tr> <td>clf</td> </tr> </tbody> </table> </td> </tr> <tr> <td>Peruvian</td> <td> <table> <tbody> <tr> <td>Male</td> </tr> <tr> <td>Female</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>20</td> </tr> <tr> <td>18</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>2917</td> </tr> <tr> <td>2529</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>pem</td> </tr> <tr> <td>pef</td> </tr> </tbody> </table> </td> </tr> <tr> <td>Venezuelan</td> <td> <table> <tbody> <tr> <td>Male</td> </tr> <tr> <td>Female</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>12</td> </tr> <tr> <td>10</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>1754</td> </tr> <tr> <td>1463</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>vem</td> </tr> <tr> <td>vef</td> </tr> </tbody> </table> </td> </tr> <tr> <td>Argentinian</td> <td> <table> <tbody> <tr> <td>Male</td> </tr> <tr> <td>Female</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>12</td> </tr> <tr> <td>30</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>1670</td> </tr> <tr> <td>3790</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>arm</td> </tr> <tr> <td>arf</td> </tr> </tbody> </table> </td> </tr> <tr> <td>Total</td> <td> </td> <td>162</td> <td>22816</td> <td> </td> </tr> </tbody> </table> <p> </p> <p>The bonafide samples were obtained from the following sources:</p> <ul> <li>Colombian accents: <a href="https://www.openslr.org/72/">https://www.openslr.org/72/</a> (<a href="https://www.openslr.org/resources/75/LICENSE">License</a>)</li> <li>Chilean accents: <a href="https://www.openslr.org/71/">https://www.openslr.org/71/</a> (<a href="https://www.openslr.org/resources/75/LICENSE">License</a>)</li> <li>Peruvian accents: <a href="https://www.openslr.org/73/">https://www.openslr.org/73/</a> (<a href="https://www.openslr.org/resources/75/LICENSE">License</a>)</li> <li>Venezuelan accents: <a href="https://www.openslr.org/75/">https://www.openslr.org/75/</a> (<a href="https://www.openslr.org/resources/75/LICENSE">License</a>)</li> <li>Argentinian accents: <a href="https://www.openslr.org/61/">https://www.openslr.org/61/</a> (<a href="https://www.openslr.org/resources/75/LICENSE">License</a>)</li> </ul> <p> </p> <p>The strategies used to generate the spoof samples:</p> <p> Table 2. Spoof Samples distribution</p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Type</strong></td> <td><strong>#Samples</strong></td> </tr> <tr> <td>StarGAN</td> <td>Voice conversion</td> <td>16000</td> </tr> <tr> <td>CycleGAN</td> <td>Voice conversion</td> <td>16000</td> </tr> <tr> <td>Diffusion</td> <td>Voice conversion</td> <td>16000</td> </tr> <tr> <td>TTS</td> <td>Text-to-speech</td> <td>5000</td> </tr> <tr> <td>TTS-StarGAN</td> <td>Text-to-speech / Voice conversion</td> <td>2500</td> </tr> <tr> <td>TTS-Diff</td> <td>Text-to-speech / Voice conversion</td> <td>2500</td> </tr> </tbody> </table> <p> </p> <ul> <li><a href="https://arxiv.org/abs/1806.02169">StarGAN-VC: Non-parallel many-to-many Voice Conversion Using Star Generative Adversarial Networks</a></li> <li><a href="https://ieeexplore.ieee.org/document/8553236">Cyclegan-VC: Non-parallel voice conversion using cycle-consistent adversarial networks</a></li> <li><a href="https://arxiv.org/abs/2109.13821">Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme</a></li> <li>TTS: Microsoft azure TTS</li> <li>TTS-VC: Microsoft azure TTS + StarGAN/Diff</li> </ul> <p> </p> <p> Table 3. Dataset overview</p> <table> <tbody> <tr> <td><strong>Audio Samples</strong></td> <td><strong>Human Speakers</strong></td> <td><strong>Spoofing algorithms</strong></td> <td><strong>Sampling rate</strong></td> </tr> <tr> <td> <table> <tbody> <tr> <td>Bonafide</td> <td>Spoof</td> </tr> <tr> <td>22816</td> <td>58000</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>Male</td> <td>Female</td> </tr> <tr> <td>78</td> <td>84</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>VC</td> <td>TTS</td> <td>VC and TTS</td> </tr> <tr> <td>3</td> <td>1</td> <td>2</td> </tr> </tbody> </table> </td> <td>16kHz</td> </tr> </tbody> </table> <p> </p> <p>On the <em>protocol.txt</em> file is listed all the files with the following structure:</p> <p><em> Subject_id file_name – spoof_type Label</em></p> <p>Consider this line on protocol.txt file:<br> <em>arf_00295 StarGAN-arf _00295_01349969200-cof _03349 _0077577 - StarGAN spoof</em><br> The first part (arf_00295) represent the subject id, from which we can also identify the accent and the gender (see nomenclature column on Table 1). The file name identify the type of spoof following for the source audio file and the target file. StarGAN represents the type of spoof. According to the table 2, this method is a Voice Conversion algorithm. If the file is a bonafide sample, we replace the <em>spoof_type</em> with a dash (-). Finally at the end of the line we refer the kind of label of the file, in the example, the file corresponds to a spoof case.</p> <p>Each zip file contains 6 folders, each one holds a type of samples. For the voice conversion folders, there are 25 sub-folders that indicate the conversion between accents. For example, Argentina-Venezuela folder indicates that the source accent of the file is Argentinian and the target is Venezuelan accent. Inside the folder there are 64 sub-folders that represent the subjects used for the conversion. For instance, the folder arf_00295-vem_04310 means that the source is an Argentinean female and the target is a Venezuelan male (see Table 1 for nomenclature). In the case of a Text-to-Speech folder there are 5 sub-folders that represent the accents. A TTS-VC folder there are 2 sub-folders that represent the voice conversion strategy used. Inside there are other sub-folders for the different combinations of source and target accents.</p> <p>You can check the folder tree structure in the tree.txt file. Table 3 shows a summary of the resulting dataset.</p> <p> </p>
Datasets: Spying through your voice assistants: Realistic voice command fingerprinting
<p>These datasets are used in our work titled "Spying through your voice assistants: Realistic voice command fingerprinting" published in USENIX Security 2023</p>
Efficacy of Voice Treatment for Parkinson's Disease
ClinicalTrials.gov study NCT00123084. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Pacifier Activated Device and Mother's Voice in Infants at High-risk for Cerebral Palsy
ClinicalTrials.gov study NCT03230032. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
The Effects of Listening to Fairy Tales, Listening to Fairy Tales in Their Mothers' Voices, and Watching Cartoons on Pain, Comfort Levels, and Physiological Parameters During Tracheostomy Care in a Pa
ClinicalTrials.gov study NCT07210346. IPD Sharing: NO. Countries: 1. Publications: 1.
Expectations of People With Type 1 Diabetes About Voice-based Interventions to Support Diabetes Distress Management
ClinicalTrials.gov study NCT05517772. IPD Sharing: NO. Countries: 1. Publications: 6.
ElderTree Via a Voice Activated Device for Managing Chronic Health Conditions (NHLBI)
ClinicalTrials.gov study NCT05240534. IPD Sharing: NO. Countries: 1. Publications: 1.
Neurofilament Light Chain And Voice Acoustic Analyses In Dementia Diagnosis
ClinicalTrials.gov study NCT06339190. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Complete Vocal Technique Voice Therapy for Muscle Tension Dysphonia (CVT4MTD)
ClinicalTrials.gov study NCT05365126. IPD Sharing: NO. Countries: 1. Publications: 2.
Creating and Assessing a Voice Dataset for Automated Classification of Chronic Obstructive Pulmonary Disease
ClinicalTrials.gov study NCT05897944. IPD Sharing: NO. Countries: 1. Publications: 1.
The ECHO Study: Compassion-focused Therapy for Young Voice Hearers and Their Caregivers
ClinicalTrials.gov study NCT07314515. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Efficacy and Safety of Voice Inception Technique Based U-healthcare Service in Patients With Type 2 Diabetes Mellitus
ClinicalTrials.gov study NCT01891474. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Mother's Recorded Voice for Preterm Infants
ClinicalTrials.gov study NCT04559620. IPD Sharing: NO. Countries: 1. Publications: 4.
Implementation of Foot Thermometry and SMS and Voice Messaging to Prevent Diabetic Foot Ulcer
ClinicalTrials.gov study NCT02373592. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Randomized Controlled Trial of Voice on Children With Vocal Nodules
ClinicalTrials.gov study NCT01255735. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Effect of Maternal Voice on Physiological Indicators and Feeding Performance
ClinicalTrials.gov study NCT07068581. IPD Sharing: NO. Countries: 1. Publications: 5.
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
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DANDI Archive for NWB datasets
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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.