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859 results for “Speeches”
Peripheral and Central Influences on Auditory Temporal Processing & Speech Perception in Older Cochlear Implantees
ClinicalTrials.gov study NCT05554692. IPD Sharing: YES. Countries: 1. Publications: 0.
Speech Motor Learning and Retention (Aim 3)
ClinicalTrials.gov study NCT06492252. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: The visual speech head start improves perception and reduces superior temporal cortex responses to auditory speech
Open the record for dataset details and reuse information.
Fast Wavenet - Non-overfitting Results - Speech - Different Speaker
<p>The result is predicted by Fast Wavenet.</p>
Fast Wavenet - Overfitting Results - Speech
<p>The result is predicted by Fast Wavenet.</p>
Fast Wavenet - Non-overfitting Results - Speech
<p>The result is predicted by Fast Wavenet.</p>
Oxford-102 Speech Captions
<p>Speech captions (synthesized by Tacotron2) of Oxford-102 using their original textual captions.</p>
Developing an English course for beginners with Part of Speech material using Google Classroom
<p>Self-paced learning</p>
STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions
<p>We present STT4SG-350 (Speech-to-Text for Swiss German), a corpus of Swiss German speech, annotated with Standard German text at the sentence level. The data is collected using a web app in which the speakers are shown Standard German sentences, which they translate to Swiss German and record. We make the corpus publicly available. It contains 343 hours of speech from all dialect regions and is the largest public speech corpus for Swiss German to date. Application areas include automatic speech recognition (ASR), text-to-speech, dialect identification, and speaker recognition. Dialect information, age group, and gender of the 316 speakers are provided. Genders are equally represented and the corpus includes speakers of all ages. Roughly the same amount of speech is provided per dialect region, which makes the corpus ideally suited for experiments with speech technology for different dialects. We provide training, validation, and test splits of the data. The test set consists of the same spoken sentences for each dialect region and allows a fair evaluation of the quality of speech technologies in different dialects. We train an ASR model on the training set and achieve an average BLEU score of 74.7 on the test set. The model beats the best published BLEU scores on 2 other Swiss German ASR test sets, demonstrating the quality of the corpus.</p>
SYNCRETISM OF THE MEDIA OF SPEECH INFLUENCE (ADVERTISING AS AN EXAMPLE)
Open the record for dataset details and reuse information.
Inner Speech Recognition for Mutism and Speech Disorder Using Brain-Computer Interface
<p>Four native English speakers, right-handed and healthy individuals participated in collecting EEG-based inner speech data in this study, all of them had no neurological or movement disorders, no hearing loss, and no speech loss. The participants were two males and two females aged 20 to 56 and were named (sub-01) for the first subject (sub-02) for the second subject and so on. A total of 400 recordings sessions were successfully completed, 100 recordings for the command Up, 100 recordings for Down, 100 recordings for the command Left, and 100 recordings for Right.</p>
Profiling Hate Speech Spreaders on Twitter
<p><strong>Task</strong></p> <p>Hate speech (HS) is commonly defined as any communication that disparages a person or a group on the basis of some characteristic such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristics. Given the huge amount of user-generated contents on Twitter, the problem of detecting, and therefore possibly contrasting the HS diffusion, is becoming fundamental, for instance for fighting against misogyny and xenophobia. To this end, in this task, we aim at identifying possible hate speech spreaders on Twitter as a first step towards preventing hate speech from being propagated among online users.</p> <p>After having addressed several aspects of author profiling in social media from 2013 to 2020 (fake news spreaders, bot detection, age and gender, also together with personality, gender and language variety, and gender from a multimodality perspective), this year we aim at investigating if it is possible to discriminate authors that have shared some hate speech in the past from those that, to the best of our knowledge, have never done it.</p> <p>As in previous years, we propose the task from a <strong>multilingual</strong> perspective:</p> <ul> <li>English</li> <li>Spanish</li> </ul> <p><strong>NOTE:</strong> Although we recommend participating in both languages (English and Spanish), it is possible to address the problem just for one language.</p> <p>Award</p> <p>We are happy to announce that the best performing team at the 9th International Competition on Author Profiling will be awarded 300,- Euro sponsored by <a href="https://www.symanto.net/"><strong>Symanto</strong></a></p> <p><strong>Data</strong></p> <p><strong>Input</strong></p> <p>The uncompressed dataset consists of a folder per language (en, es). Each folder contains:</p> <ul> <li>An XML file per author (Twitter user) with 100 tweets. The name of the XML file corresponding to the unique author id.</li> <li>A truth.txt file with the list of authors and the ground truth.</li> </ul> <p>The format of the XML files is:</p> <pre> <author lang="en"> <documents> <document>Tweet 1 textual contents</document> <document>Tweet 2 textual contents</document> ... </documents> </author> </pre> <p>The format of the truth.txt file is as follows. The first column corresponds to the author id. The second column contains the truth label.</p> <pre> b2d5748083d6fdffec6c2d68d4d4442d:::0 2bed15d46872169dc7deaf8d2b43a56:::0 8234ac5cca1aed3f9029277b2cb851b:::1 5ccd228e21485568016b4ee82deb0d28:::0 60d068f9cafb656431e62a6542de2dc0:::1 ... </pre> <p><strong>Output</strong></p> <p>Your software must take as input the absolute path to an unpacked dataset, and has to output for each document of the dataset a corresponding XML file that looks like this:</p> <pre> <author id="author-id" lang="en|es" type="0|1" /> </pre> <p>The naming of the output files is up to you. However, we recommend using the author-id as filename and "XML" as an extension.</p> <p><strong>IMPORTANT!</strong> Languages should not be mixed. A folder should be created for each language and place inside only the files with the prediction for this language.</p> <p><strong>Evaluation</strong></p> <p>The performance of your system will be ranked by accuracy. For each language, we will calculate individual accuracies in discriminating between the two classes. Finally, we will average the accuracy values per language to obtain the final ranking.</p> <p><strong>Related Work</strong></p> <ul> <li>[1] Valerio Basile, Cristina Bosco, Elisabetta Fersini, Dora Nozza, Viviana Patti, Francisco Rangel, Paolo Rosso, Manuela Sanguinetti (2019). <a href="http://personales.upv.es/prosso/resources/BasileEtAl_SemEval19.pdf">SemEval-2019 task 5: Multilingual detection of hate speech against immigrants and women in Twitter. </a>Proc. SemEval 2019</li> <li>[2] Fabio Poletto, Valerio Basile, Manuela Sanguinetti, Cristina Bosco, Viviana Patti (2020). <a href="https://link.springer.com/article/10.1007/s10579-020-09502-8">Resources and benchmark corpora for hate speech detection: a systematic review. </a>Language Resources & Evaluation. https://doi.org/10.1007/s10579-020-09502-8</li> <li>[3] Paula Fortuna, Sérgio Nunes (2018). <a href="https://dl.acm.org/doi/10.1145/3232676">A survey on automatic detection of hate speech in text. </a>ACM Computing Surveys (CSUR) 51.4</li> <li>[4] Maria Anzovino, Elisabetta Fersini, Paolo Rosso (2018). <a href="https://link.springer.com/chapter/10.1007/978-3-319-91947-8_6">Automatic Identification and Classification of Misogynistic Language on Twitter. </a>In: Proc. 23rd Int. Conf. on Applications of Natural Language to Information Systems, NLDB-2018, Springer-Verlag, LNCS(10859), pp. 57-64</li> <li>[5] Elisabetta Fersini, Paolo Rosso, Maria Anzovino (2018). <a href="http://personales.upv.es/prosso/resources/FersiniEtAl_IberEval18.pdf">Overview of the task on automatic misogyny identification at IberEval 2018. </a>Proc. IberEval 2018</li> <li>[6] Elisabetta Fersini, Dora Nozza, Paolo Rosso (2018). <a href="http://personales.upv.es/prosso/resources/FersiniEtAl_Evalita18.pdf">Overview of the Evalita 2018 task on automatic misogyny identification (AMI). Proc. </a>EVALITA 2018</li> <li>[7] Cristina Bosco, Felice Dell'Orletta, Fabio Poletto, Manuela Sanguinetti, Maurizio Tesconi (2018). <a href="https://pdfs.semanticscholar.org/3eae/e4b2b8d9c7de52ba2386c73bb30097ec111c.pdf">Overview of the EVALITA 2018 hate speech detection task. </a>Proc. EVALITA 2018</li> <li>[8] Samuel Caetano da Silva, Thiago Castro Ferreira, Ricelli Moreira Silva Ramos, Ivandre Paraboni (2020). <a href="https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/3478">Data-driven and psycholinguistics motivated approaches to hate speech detection. </a>Computación y Sistemas, 24(3): 1179–1188</li> <li>[9] Stiven Zimmerman, Udo Kruschwitz, Cris Fox (2018). <a href="https://www.aclweb.org/anthology/L18-1404.pdf">Improving hate speech detection with deep learning ensembles. </a>In Proc. of the Eleventh Int. Conf. on Language Resources and Evaluation (LREC 2018)</li> <li>[10] Francisco Rangel, Anastasia Giachanou, Bilal Ghanem, Paolo Rosso. <a href="http://ceur-ws.org/Vol-2696/paper_267.pdf">Overview of the 8th Author Profiling Task at PAN 2020: Profiling Fake News Spreaders on Twitter. </a>In: L. Cappellato, C. Eickhoff, N. Ferro, and A. Névéol (eds.) CLEF 2020 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings.CEUR-WS.org, vol. 2696</li> <li>[11] Francisco Rangel and Paolo Rosso. <a href="http://ceur-ws.org/Vol-2380/paper_263.pdf">Overview of the 7th Author Profiling Task at PAN 2019: Bots and Gender Profiling in Twitter. </a>In: L. Cappellato, N. Ferro, D. E. Losada and H. Müller (eds.) CLEF 2019 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings.CEUR-WS.org, vol. 2380</li> <li>[12] Francisco Rangel, Paolo Rosso, Martin Potthast, Benno Stein. <a href="http://ceur-ws.org/Vol-2125/invited_paper_15.pdf">Overview of the 6th author profiling task at pan 2018: multimodal gender identification in Twitter.</a> In: CLEF 2018 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 2125.</li> <li>[13] Francisco Rangel, Paolo Rosso, Martin Potthast, Benno Stein. <a href="http://ceur-ws.org/Vol-1866/invited_paper_11.pdf">Overview of the 5th Author Profiling Task at PAN 2017: Gender and Language Variety Identification in Twitter.</a> In: Cappellato L., Ferro N., Goeuriot L, Mandl T. (Eds.) CLEF 2017 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 1866.</li> <li>[14] Francisco Rangel, Paolo Rosso, Ben Verhoeven, Walter Daelemans, Martin Pottast, Benno Stein. <a href="http://ceur-ws.org/Vol-1609/16090750.pdf">Overview of the 4th Author Profiling Task at PAN 2016: Cross-Genre Evaluations.</a> In: Balog K., Capellato L., Ferro N., Macdonald C. (Eds.) CLEF 2016 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 1609, pp. 750-784</li> <li>[15] Francisco Rangel, Fabio Celli, Paolo Rosso, Martin Pottast, Benno Stein, Walter Daelemans. <a href="http://personales.upv.es/prosso/resources/RangelEtAl_PAN15.pdf%22">Overview of the 3rd Author Profiling Task at PAN 2015.</a>In: Linda Cappelato and Nicola Ferro and Gareth Jones and Eric San Juan (Eds.): CLEF 2015 Labs and Workshops, Notebook Papers, 8-11 September, Toulouse, France. CEUR Workshop Proceedings. ISSN 1613-0073, http://ceur-ws.org/Vol-1391/,2015.</li> <li>[16] Francisco Rangel, Paolo Rosso, Irina Chugur, Martin Potthast, Martin Trenkmann, Benno Stein, Ben Verhoeven, Walter Daelemans. <a href="http://ceur-ws.org/Vol-1180/CLEF2014wn-Pan-RangelEt2014.pdf">Overview of the 2nd Author Profiling Task at PAN 2014.</a> In: Cappellato L., Ferro N., Halvey M., Kraaij W. (Eds.) CLEF 2014 Labs and Workshops, Notebook Papers. CEUR-WS.org, vol. 1180, pp. 898-827.</li> <li>[17] Francisco Rangel, Paolo Rosso, Moshe Koppel, Efstatios Stamatatos, Giacomo Inches. <a href="http://ceur-ws.org/Vol-1179/CLEF2013wn-PAN-RangelEt2013.pdf">Overview of the Author Profiling Task at PAN 2013.</a> In: Forner P., Navigli R., Tufis D. (Eds.)Notebook Papers of CLEF 2013 LABs and Workshops. CEUR-WS.org, vol. 1179</li> <li>[18] Francisco Rangel and Paolo Rosso <a href="https://ojs.letras.up.pt/ojs/index.php/LLLD/article/download/6119/5761">On the Implications of the General Data Protection Regulation on the Organisation of Evaluation Tasks. </a>In: Language and Law / Linguagem e Direito, Vol. 5(2), pp. 80-102</li> <li>[19] Francisco Rangel, Marc Franco-Salvador, Paolo Rosso <a href="https://arxiv.org/abs/1705.10754">A Low Dimensionality Representation for Language Variety Identification. </a>In: Postproc. 17th Int. Conf. on Comput. Linguistics and Intelligent Text Processing, CICLing-2016, Springer-Verlag, Revised Selected Papers, Part II, LNCS(9624), pp. 156-169 (arXiv:1705.10754)</li> </ul>
Liberation speech May 1945
<u>Source</u>: Europeana <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1652249795.3708.jpg">https://4dcity.org/imgupload/1652249795.3708.jpg</a> <br><u>Original Image URL</u>: <a href="https://api.europeana.eu/thumbnail/v2/url.json?uri=https%3A%2F%2Fwww.openbeelden.nl%2Fimages%2F778883%2FBevrijdingstoespraak_mei_1945_%25283_46%2529.png&type=VIDEO">https://api.europeana.eu/thumbnail/v2/url.json?uri=https%3A%2F%2Fwww.openbeelden.nl%2Fimages%2F778883%2FBevrijdingstoespraak_mei_1945_%25283_46%2529.png&type=VIDEO</a> <br><br><u>Image-Metadata:</u><br>Filename: 1652249795.3708.jpg<br>Image Dimensions: 400x225<br>Megapixels: 0.09 MP<br>Filesize: 161.13 KB<br>
EWA-DB – Early Warning of Alzheimer speech database
<h4>EWA-DB is a speech database that contains data from 3 clinical groups: Alzheimer's disease, Parkinson's disease, mild cognitive impairment, and a control group of healthy subjects. Speech samples of each clinical group were obtained using the EWA smartphone application, which contains 4 different language tasks: sustained vowel phonation, diadochokinesis, object and action naming (30 objects and 30 actions), picture description (two single pictures and three complex pictures).<br><br>The total number of speakers in the database is 1649. Of these, there are 87 people with Alzheimer's disease, 175 people with Parkinson's disease, 62 people with mild cognitive impairment, 2 people with a mixed diagnosis of Alzheimer's + Parkinson's disease and 1323 healthy controls.<br><br>For speakers who provided written consent (total number of 1003 speakers), we publish audio recordings in WAV format. We are also attaching a JSON file with ASR transcription, if available manual annotation (available for 965 speakers) and additional information about the speaker. For speakers who did not give their consent to publish the recording, only the JSON file is provided. ASR transcription is provided for all 1649 speakers. All 1649 speakers gave their consent to the provider to process their audio recordings. Therefore, it is possible for third party researchers to carry out their experiments also on the unpublished audio recordings through cooperation with the provider.</h4>
NeuroVoz: a Castillian Spanish corpus of parkinsonian speech
<p>The NeuroVoz dataset emerges as a pioneering resource in the field of computational linguistics and biomedical research, specifically designed to enhance the diagnosis and understanding of Parkinson's Disease (PD) through speech analysis. This dataset is distinguished as the first of its kind to be made publicly available in Castilian Spanish, addressing a critical gap in the availability of linguistic and dialectical diversity within PD research.</p> <p>Compiled from a cohort of 112 participants, including 54 individuals diagnosed with PD and 58 healthy controls, the NeuroVoz dataset offers a rich compilation of speech recordings. All PD participants were recorded under medication (ON state), ensuring consistency and reliability in the speech samples collected. The dataset is meticulously curated to include a variety of speech tasks—ranging from sustained vowel phonations and diadochokinetic (DDK) tests to 16 structured listen-and-repeat utterances and spontaneous monologues. The inclusion of both manually transcribed listen-and-repeat tasks and Whisper-automated transcriptions for monologues underscores our commitment to data accuracy and usability.</p> <p>Encompassing 2,977 audio files, the NeuroVoz dataset provides an extensive repository, averaging 26.88 +- 3.35 recordings per participant, making it an invaluable asset for researchers seeking to explore the nuances of PD-affected speech. The dataset's structure and composition facilitate a multifaceted analysis of speech impairments associated with PD, offering insights into phonatory, articulatory, and prosodic changes.</p> <p>In contributing to the body of knowledge with the NeuroVoz dataset, we invite the scientific community to engage with this dataset, explore the specific speech characteristics of PD in Castilian Spanish speakers, and advance the field of PD diagnosis through innovative speech analysis techniques.</p> <p> </p> <p>If you use this dataset, please cite both this Zenodo and the article describing the corpus:</p> <ul> <li>Mendes-Laureano, J., Gómez-García, J.A., Guerrero-López, A. <em>et al.</em> NeuroVoz: a Castillian Spanish corpus of parkinsonian speech. <em>Sci Data</em> <strong>11</strong>, 1367 (2024). https://doi.org/10.1038/s41597-024-04186-z</li> <li>Zenodo dataset: Mendes-Laureano, J., Gómez-García, J. A., Guerrero-López, A., Luque-Buzo, E., Arias-Londoño, J. D., Grandas-Pérez, F. J., & Godino Llorente, J. I. (2024). NeuroVoz: a Castillian Spanish corpus of parkinsonian speech (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10777657</li> </ul>
Dataset for "A high-performance neuroprosthesis for speech decoding and avatar control"
<p>The dataset associated with the publication: <a href="https://www.nature.com/articles/s41586-023-06443-4">"A high-performance neuroprosthesis for speech decoding and avatar control"</a></p>
A lightweight speech recognition method with target-swap knowledge distillation for Mandarin air traffic control communications
<p>Mandarin air traffic control communications (ATCC) dataset for the paper "A lightweight speech recognition method with target-swap knowledge distillation for Mandarin air traffic control communications".</p>
Prediction of Amyloid and Mild Cognitive Impairment in Early Stage Alzheimer's Disease From Remote Speech Phenotyping
ClinicalTrials.gov study NCT04928690. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Speech Perception Impairment Follow Complete Recovery of Sudden Sensorineural Hearing Loss
ClinicalTrials.gov study NCT05608161. IPD Sharing: NO. Countries: 0. Publications: 0.
Laryngeal Adaptation for Speech and Swallowing
ClinicalTrials.gov study NCT03770377. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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)
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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.